Recombinant Salmonella typhimurium Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA) in Salmonella typhimurium?

Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA) in Salmonella typhimurium is an enzyme that plays a crucial role in cell wall biosynthesis. It functions as a glycan polymerase, catalyzing the polymerization of peptidoglycan glycan strands during bacterial cell wall formation. The protein is encoded by the mtgA gene (also known as STM3326) and consists of 242 amino acids in its full-length form. As a peptidoglycan glycosyltransferase, it is essential for maintaining cell wall integrity and bacterial cell shape .

How is recombinant Salmonella typhimurium mtgA typically produced for research?

Recombinant Salmonella typhimurium mtgA is typically produced through heterologous expression in Escherichia coli expression systems. The full-length mtgA gene (encoding amino acids 1-242) is cloned into an appropriate expression vector that incorporates an N-terminal His-tag for purification purposes. After transformation into E. coli, protein expression is induced, and the cells are harvested and lysed. The recombinant protein is then purified using affinity chromatography (typically Ni-NTA resin), taking advantage of the His-tag. Following purification, the protein is often lyophilized for storage stability. For optimal results, researchers should reconstitute the lyophilized protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL, with the addition of 5-50% glycerol for long-term storage at -20°C/-80°C .

What are the recommended storage conditions for recombinant mtgA protein?

For optimal stability and activity of recombinant Salmonella typhimurium mtgA protein, the following storage conditions are recommended:

• Store the lyophilized powder at -20°C/-80°C upon receipt

• For reconstituted protein, avoid repeated freeze-thaw cycles as they can lead to protein denaturation and loss of activity

• Working aliquots can be stored at 4°C for up to one week

• For long-term storage, add 5-50% glycerol (final concentration) to the reconstituted protein and store in small aliquots at -20°C/-80°C

• Use Tris/PBS-based buffer with 6% Trehalose at pH 8.0 as the storage buffer

How should I design experiments to study the role of mtgA in antimicrobial resistance in Salmonella typhimurium?

When designing experiments to study the role of mtgA in antimicrobial resistance in Salmonella typhimurium, consider a multi-faceted approach:

• Gene Knockout Studies: Create mtgA knockout strains using CRISPR-Cas9 or homologous recombination techniques and compare their antimicrobial susceptibility profiles with wild-type strains. This true experimental design requires randomized control and treatment groups with proper controls .

• Complementation Assays: Reintroduce the wild-type mtgA gene into knockout strains to confirm that observed phenotypes are directly attributable to mtgA function.

• Site-Directed Mutagenesis: Generate specific mutations in the mtgA gene to identify key residues involved in antimicrobial resistance mechanisms.

• Expression Analysis: Use RT-qPCR to quantify mtgA expression levels under different antibiotic stress conditions.

• Antimicrobial Susceptibility Testing: Perform broth microdilution or disk diffusion assays according to CLSI guidelines to determine minimum inhibitory concentrations (MICs) for various antibiotics.

Include proper controls in each experiment, such as reference strains with known resistance profiles, and ensure statistical robustness through adequate biological and technical replicates. Phylogenetic analysis can also provide context regarding the evolutionary aspects of mtgA in relation to antimicrobial resistance mechanisms .

What methodological approaches can be used to analyze the interaction between mtgA and other cell wall biosynthesis enzymes?

To analyze interactions between mtgA and other cell wall biosynthesis enzymes, implement these methodological approaches:

• Protein-Protein Interaction Assays:

  • Co-immunoprecipitation (Co-IP): Use anti-mtgA antibodies to pull down mtgA along with interacting proteins, followed by mass spectrometry identification.

  • Bacterial Two-Hybrid (B2H) Assays: Express mtgA fused to one domain of a split transcription factor and potential interacting partners fused to the complementary domain.

  • Surface Plasmon Resonance (SPR): Measure direct binding kinetics between purified mtgA and candidate interacting proteins.

• Functional Complementation Studies:

  • Use chimeric proteins combining domains from mtgA and other peptidoglycan biosynthesis enzymes to determine functional relationships.

  • Express the recombinant protein in heterologous systems lacking endogenous mtgA activity.

• Structural Biology Approaches:

  • X-ray Crystallography or Cryo-EM: Determine the structure of mtgA alone and in complex with interacting partners.

  • Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS): Map regions of mtgA involved in protein-protein interactions.

• In silico Analyses:

  • Conduct molecular docking simulations to predict interaction interfaces.

  • Use sequence co-evolution analysis to identify co-evolving residues that may indicate functional interactions.

These approaches should be implemented using proper experimental design principles, including randomization, replication, and appropriate controls to ensure robust and reproducible results .

How does the phylogenomic structure of Salmonella Typhimurium influence mtgA function in antimicrobial resistance?

The phylogenomic structure of Salmonella Typhimurium significantly influences mtgA function in antimicrobial resistance through several interconnected mechanisms:

Clonal Lineage Influence:
Salmonella Typhimurium isolates cluster into distinct phylogenetic clones (ST36, ST313, ST19, and ST34), each with different antimicrobial resistance profiles. The ST34 lineage, particularly prevalent in China, has been further divided into two clades (ST34C1 and ST34C2). The ST34C2 clade, which appears to have originated in Shanghai before expanding nationally, carries specific antimicrobial resistance determinants that may affect mtgA function, including extended-spectrum β-lactamase genes like blaCTX-M-14 and mutations in quinolone resistance-determining regions .

Horizontal Gene Transfer Mechanisms:
The function of mtgA can be modulated by horizontally transferred genetic elements. For example, the blaCTX-M-14 gene found in ST34C2 clade isolates is often linked to ISEcp1 upstream and ΔIS903B downstream on IncI(Gamma)-like plasmids. These plasmid-mediated resistance mechanisms can indirectly influence mtgA function by altering cell wall structures or stress responses .

Temporal Trends in Resistance:
Longitudinal studies have documented increasing multidrug resistance (MDR) in S. Typhimurium over time, with MDR rates in 2017-2018 significantly higher than in 2010 (p<0.05). This temporal pattern suggests evolutionary pressure on cell wall biosynthesis pathways, potentially affecting mtgA function and expression in response to antimicrobial selection pressure .

To properly investigate these relationships, researchers should employ a combination of whole-genome sequencing, phylogenetic analysis, and phenotypic antimicrobial susceptibility testing across temporally and geographically diverse isolate collections.

What experimental controls should be implemented when studying the enzymatic activity of recombinant mtgA?

When studying the enzymatic activity of recombinant Salmonella typhimurium mtgA, implement these essential experimental controls:

• Negative Controls:

  • Heat-inactivated mtgA enzyme (95°C for 10 minutes) to establish baseline readings

  • Reaction mixture without enzyme to account for non-enzymatic reactions

  • Substrate-only controls to monitor auto-polymerization or degradation

• Positive Controls:

  • Commercial glycosyltransferase with known activity on the same substrate

  • Previously characterized batch of recombinant mtgA with established activity

  • E. coli PBP1A or other well-characterized peptidoglycan glycosyltransferase

• Specificity Controls:

  • Non-cognate substrates to verify enzyme specificity

  • Structurally similar but non-functional substrate analogs

  • Graduated substrate concentrations to establish kinetic parameters

• Inhibition Controls:

  • Known glycosyltransferase inhibitors (e.g., moenomycin) at standardized concentrations

  • Divalent cation chelators (EDTA) to verify metal ion dependence

  • pH range tests to determine optimal reaction conditions

• Technical Controls:

  • Multiple protein batches to account for preparation variability

  • Time-course measurements to ensure linearity of enzyme activity

  • Buffer composition controls to rule out assay interference

These controls should be implemented following true experimental design principles, with proper randomization and replication to ensure statistical validity .

How can researchers effectively analyze contradictory data on mtgA function in different Salmonella strains?

When faced with contradictory data on mtgA function across different Salmonella strains, researchers should employ a structured analytical approach:

When reporting results, clearly document all methodological details, strain characteristics, and experimental conditions to facilitate comparison and reproducibility across studies .

What are the optimal conditions for enzymatic assays to measure mtgA transglycosylase activity?

The optimal conditions for enzymatic assays to measure mtgA transglycosylase activity include:

Buffer Composition and pH:

• 50 mM HEPES or MES buffer at pH 7.5-8.0

• 10-25 mM MgCl₂ (essential divalent cation)

• 100-150 mM NaCl to maintain ionic strength

• 0.1% Triton X-100 or CHAPS as a mild detergent to maintain protein solubility

Temperature and Incubation Time:

• Maintain reaction temperature at 30-37°C (optimal 37°C for physiological relevance)

• Monitor reaction progress at intervals over 30-60 minutes to establish linearity

• Pre-incubate enzyme for 5-10 minutes before initiating reaction with substrate

Substrate Considerations:

• Use radiolabeled or fluorescently tagged lipid II as substrate

• Maintain substrate concentration within 1-10 μM range depending on assay sensitivity

• Prepare substrate in appropriate detergent micelles to ensure accessibility

Detection Methods:

• Radioactive assay: Using [¹⁴C]-labeled lipid II, followed by paper chromatography or SDS-PAGE

• FRET-based assay: Using doubly-labeled lipid II substrates to monitor polymerization

• HPLC analysis: To separate and quantify reaction products

Data Analysis:

• Calculate reaction velocities from the linear portion of progress curves

• Determine kinetic parameters (Km, Vmax) using non-linear regression analysis

• Express specific activity as nmol product formed per minute per mg of enzyme

Robust statistical approaches including technical triplicates and biological replicates should be employed to ensure reproducibility and reliability of the enzymatic data .

How can researchers analyze the structural dynamics of mtgA using computational approaches?

Researchers can analyze the structural dynamics of mtgA using these computational approaches:

• Homology Modeling and Structure Prediction:

  • Generate 3D models of mtgA using AlphaFold2 or SWISS-MODEL when crystal structures are unavailable

  • Validate models using Ramachandran plots, QMEAN, and ProCheck

  • Compare with structures of homologous proteins from related bacteria

• Molecular Dynamics (MD) Simulations:

  • Perform all-atom MD simulations (100-1000 ns) using GROMACS, AMBER, or NAMD

  • Apply appropriate force fields (e.g., CHARMM36, AMBER ff14SB) for protein in membrane environments

  • Sample conformational space using enhanced sampling techniques like replica exchange MD

• Binding Site Analysis:

  • Identify catalytic and substrate-binding pockets using CASTp, POCASA, or SiteMap

  • Analyze conserved residues across homologs to identify functionally important regions

  • Model substrate-enzyme complexes using molecular docking (AutoDock, HADDOCK)

• Normal Mode Analysis (NMA) and Elastic Network Models:

  • Identify large-scale collective motions that might be important for function

  • Determine potential allosteric sites and communication pathways

  • Analyze protein flexibility and rigidity using ProDy or DynOmics

• Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) Calculations:

  • Estimate binding free energies of substrates or inhibitors

  • Perform per-residue energy decomposition to identify key interaction hotspots

• Machine Learning Applications:

  • Apply graph neural networks to predict effects of mutations on protein stability

  • Use supervised learning to identify patterns in sequence-structure-function relationships

Implementation of these approaches requires appropriate computational resources and expertise in biomolecular simulation. Results should be validated experimentally whenever possible, for example by testing predictions through site-directed mutagenesis .

What are the best experimental approaches to study mtgA's role in cell wall integrity and morphology?

To effectively study mtgA's role in cell wall integrity and morphology, researchers should employ these complementary experimental approaches:

• Genetic Manipulation Strategies:

  • Create precise gene deletions (ΔmtgA) using λ-Red recombination or CRISPR-Cas9

  • Develop conditional expression systems (e.g., tetracycline-inducible) to control mtgA levels

  • Generate point mutations in catalytic residues to create enzymatically inactive variants

  • Construct fluorescent protein fusions for localization studies

• Morphological Analysis:

  • Electron Microscopy: Use transmission electron microscopy (TEM) to visualize cell wall thickness and ultrastructure at nanometer resolution

  • Super-Resolution Microscopy: Apply techniques like STORM or PALM to visualize mtgA localization patterns with 20-30 nm resolution

  • Atomic Force Microscopy (AFM): Measure mechanical properties of the cell wall including stiffness and elasticity

• Cell Wall Composition Analysis:

  • HPLC Analysis: Quantify muropeptide composition after enzymatic digestion of peptidoglycan

  • Mass Spectrometry: Perform detailed structural analysis of peptidoglycan fragments

  • Radioactive Labeling: Track incorporation of [³H]-diaminopimelic acid or [¹⁴C]-GlcNAc into peptidoglycan

• Phenotypic Assays:

  • Osmotic Stress Tolerance: Test survival in hypo/hyperosmotic conditions

  • β-lactam Sensitivity: Determine MICs for cell wall-targeting antibiotics

  • Growth Curve Analysis: Monitor growth rates under various stress conditions

• Protein-Protein Interaction Studies:

  • Bacterial Two-Hybrid: Screen for interaction partners within peptidoglycan synthesis machinery

  • Co-Immunoprecipitation: Validate interactions with antibodies against native proteins

  • Cross-Linking Mass Spectrometry: Identify interaction interfaces at amino acid resolution

• Live Cell Imaging:

  • Fluorescent D-Amino Acid Labeling: Visualize sites of active peptidoglycan synthesis

  • Time-Lapse Microscopy: Monitor cell division and morphological changes in real-time

These approaches should be implemented following proper experimental design principles, including appropriate controls, replication, and statistical analysis to ensure robust and reproducible results .

How should researchers interpret contradictory findings regarding mtgA expression in multidrug-resistant Salmonella strains?

When interpreting contradictory findings regarding mtgA expression in multidrug-resistant (MDR) Salmonella strains, researchers should apply this systematic analytical framework:

This structured approach enables researchers to systematically address contradictions, potentially revealing strain-specific regulatory mechanisms or identifying previously unrecognized variables affecting mtgA expression in MDR Salmonella .

What statistical approaches are most appropriate for analyzing the impact of mtgA mutations on antimicrobial resistance profiles?

When analyzing the impact of mtgA mutations on antimicrobial resistance profiles, these statistical approaches provide the most robust and informative results:

• Minimum Inhibitory Concentration (MIC) Analysis:

  • Fold-Change Calculations: Express changes in MICs as fold-differences relative to wild-type

  • Geometric Mean MICs: Calculate for multiple experiments to account for the logarithmic nature of dilution testing

  • Non-Parametric Tests: Apply Mann-Whitney U or Kruskal-Wallis tests for comparing MICs across multiple mutation types, as MIC data often violates normality assumptions

• Time-Kill Curve Analysis:

  • Area Under the Curve (AUC) Calculations: Convert time-kill curves to single numeric values for statistical comparison

  • Repeated Measures ANOVA: Analyze time-dependent changes in bacterial counts

  • Nonlinear Mixed Effects Models: Account for both fixed effects (mutation type) and random effects (experimental variation)

• Regression Modeling Approaches:

  • Poisson Generalized Linear Mixed Models: Similar to those used in the Shanghai study for temporal MDR analysis

  • Logistic Regression: For binary outcomes (resistant/susceptible) across multiple antibiotics

  • Ordinal Regression: When categorizing resistance into multiple ordered levels (susceptible, intermediate, resistant)

• Multivariate Analyses:

  • Principal Component Analysis (PCA): Reduce dimensionality when analyzing resistance to multiple antibiotic classes

  • Hierarchical Clustering: Group mutations based on similarity of resistance profiles

  • Partial Least Squares Discriminant Analysis (PLS-DA): Identify which mutations best explain observed resistance patterns

• Genetic Association Methods:

  • Genome-Wide Association Studies (GWAS): Identify mutations statistically associated with resistance phenotypes

  • Epistasis Analysis: Detect interactions between mtgA mutations and other genetic determinants

• Phylogenetic Comparative Methods:

  • Ancestral State Reconstruction: Estimate when resistance-associated mutations appeared in evolutionary history

  • Phylogenetic Generalized Least Squares (PGLS): Account for phylogenetic relationships when analyzing resistance data

Statistical significance should typically be set at p<0.05 with appropriate corrections for multiple testing (e.g., Bonferroni, Benjamini-Hochberg FDR). Effect sizes and confidence intervals should be reported alongside p-values to provide information about biological significance .

What are the most promising directions for future research on mtgA in the context of antimicrobial resistance?

The most promising directions for future research on mtgA in the context of antimicrobial resistance include:

• Structure-Based Drug Design:

  • Leveraging structural information about mtgA to design specific inhibitors that could potentially overcome existing resistance mechanisms

  • Focusing on allosteric sites that might be less susceptible to resistance-conferring mutations

• Combination Therapy Approaches:

  • Investigating synergistic effects between mtgA inhibitors and existing antibiotics

  • Exploring how targeting mtgA might resensitize resistant Salmonella strains to current antimicrobials

• Genetic Circuit Manipulation:

  • Developing CRISPR-based antimicrobials targeting mtgA and related genes

  • Engineering synthetic genetic circuits to exploit mtgA regulation for controlling bacterial populations

• Evolutionary Studies:

  • Tracking the emergence and spread of mtgA variants in the context of the ST34C2 clade and other high-risk lineages

  • Conducting experimental evolution studies to predict potential resistance mechanisms against mtgA inhibitors

• One Health Surveillance:

  • Implementing comprehensive surveillance programs that track mtgA variants across human, animal, and environmental samples

  • Developing rapid diagnostic tools to identify high-risk mtgA variants in clinical settings

• Systems Biology Integration:

  • Creating comprehensive models of cell wall biosynthesis networks that include mtgA and its regulatory connections

  • Applying multi-omics approaches to understand how mtgA function changes under different antibiotic pressures

These research directions should build upon the current understanding of mtgA's role in Salmonella pathogenesis and antimicrobial resistance. Particular attention should be paid to the emergent ST34C2 clade identified in Shanghai, which has shown potential for national expansion with increased antimicrobial resistance capabilities .

How can integrative approaches enhance our understanding of mtgA's role in Salmonella pathogenesis?

Integrative approaches can significantly enhance our understanding of mtgA's role in Salmonella pathogenesis through multiple complementary strategies:

• Multi-Omics Integration:

  • Combine genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive models of mtgA function

  • Apply network analysis to identify key interactions between mtgA and other pathogenesis factors

  • Implement longitudinal sampling to capture dynamic changes during infection progression

• Host-Pathogen Interaction Studies:

  • Use cell culture infection models to compare wild-type and mtgA mutant strains

  • Apply single-cell RNA-seq to analyze host response to Salmonella with different mtgA variants

  • Develop organoid infection models that better recapitulate in vivo conditions

• Animal Model Integration:

  • Utilize multiple animal models (mice, zebrafish larvae) to validate findings across host species

  • Implement tissue-specific analyses to determine organ-specific effects of mtgA activity

  • Apply in vivo imaging technologies to track Salmonella with fluorescently tagged mtgA

• Clinical Sample Correlation:

  • Analyze mtgA sequence and expression in clinical isolates from various infection sites

  • Correlate mtgA variants with patient outcomes and treatment responses

  • Compare isolates from carrier states versus acute infections

• Phylogenomic Analysis:

  • Expand on current knowledge of the global phylogenomic structure, particularly focusing on emerging clades like ST34C2

  • Apply comparative genomics across multiple Salmonella serovars to identify mtgA conservation patterns

  • Implement phylodynamic approaches to predict future evolutionary trajectories

• Mathematical Modeling:

  • Develop in silico models of peptidoglycan biosynthesis incorporating mtgA activity

  • Create population dynamics models to predict the spread of specific mtgA variants

  • Implement machine learning to identify patterns in complex datasets

These integrative approaches would build upon existing research that has identified the increasing prevalence of MDR Salmonella Typhimurium (77.3% of isolates) and the emergence of concerning clades like ST34C2 with extended-spectrum β-lactamase capabilities . By combining multiple methodologies, researchers can develop a more comprehensive understanding of how mtgA contributes to Salmonella pathogenesis across different scales, from molecular interactions to population-level dynamics.

What are best practices for expressing and purifying recombinant mtgA for enzymatic studies?

For optimal expression and purification of recombinant Salmonella typhimurium mtgA, researchers should follow these best practices:

Expression System Selection:

• Bacterial Systems: E. coli BL21(DE3) or derivatives are preferred hosts due to compatibility with Salmonella proteins

• Expression Vectors: pET series vectors with T7 promoter systems provide strong, controllable expression

• Fusion Tags: N-terminal His6-tag facilitates purification while minimizing interference with C-terminal enzymatic domains

Protein Expression Optimization:

• Induction Parameters:

  • Temperature: Reduce to 16-20°C post-induction to enhance proper folding

  • IPTG Concentration: Use 0.1-0.5 mM for optimal expression-to-solubility ratio

  • Duration: Extended expression (16-20 hours) at lower temperatures often yields more soluble protein

• Media Optimization:

  • Use Terrific Broth (TB) with glycerol for higher cell density and protein yield

  • Add 0.2% glucose to reduce basal expression before induction

  • Supplement with additional Mg²⁺ (10 mM) to stabilize the enzyme

Purification Strategy:

• Initial Capture:

  • Ni-NTA affinity chromatography using imidazole gradient (20-250 mM)

  • Buffers should contain 0.1% detergent (CHAPS or Triton X-100) to maintain solubility

• Secondary Purification:

  • Size-exclusion chromatography (Superdex 200) to remove aggregates

  • Anion exchange chromatography (Q Sepharose) for removing contaminants

• Quality Control:

  • SDS-PAGE analysis with Coomassie staining to verify >90% purity

  • Western blotting with anti-His antibodies to confirm identity

  • Dynamic light scattering to assess monodispersity

Storage and Handling:

• Store purified protein in Tris/PBS-based buffer with 6% trehalose at pH 8.0

• Add glycerol to 50% final concentration for -20°C/-80°C storage

• Aliquot to avoid repeated freeze-thaw cycles

• For working stocks, store at 4°C for no more than one week

Implementation of these best practices will ensure high-quality recombinant mtgA suitable for downstream enzymatic studies, structural analyses, and activity assays.

What are the most important recent publications on Salmonella typhimurium mtgA and antimicrobial resistance?

While the search results don't provide a comprehensive list of publications specifically on Salmonella typhimurium mtgA and antimicrobial resistance, we can identify key recent research directions based on available information:

• Antimicrobial Resistance Surveillance Studies:
  The 2024 publication on "Dynamic antimicrobial resistance and phylogenomic structure of Salmonella enterica serovar Typhimurium" represents significant recent work in this field. This study conducted a comprehensive analysis of 277 S. Typhimurium isolates collected from 2007-2019 in Shanghai, China, revealing that 77.3% were multi-drug resistant. The research identified a concerning trend of increasing MDR, with rates in 2017-2018 significantly higher than in 2010 .

• Phylogenomic Analysis of Emergent Clades:
  Recent research has identified the global spread of specific S. Typhimurium clones (ST36, ST313, ST19, and ST34), with particular focus on the ST34 lineage prevalent in China. The discovery of a new clade (ST34C2) with extended-spectrum β-lactamase capabilities suggests evolutionary adaptation of cell wall biosynthesis mechanisms, potentially involving mtgA .

• Molecular Mechanisms of Resistance Transfer:
  Studies on plasmid-mediated resistance mechanisms have identified patterns of horizontal gene transfer, such as the blaCTX-M-14 gene linked to ISEcp1 upstream and ΔIS903B downstream on IncI(Gamma)-like plasmids. This research demonstrates how resistance determinants affecting cell wall biosynthesis can be transmitted between strains .

• Structural and Functional Analysis of Cell Wall Biosynthesis Enzymes:
  While not specifically mentioned in the search results, structural studies of peptidoglycan biosynthesis enzymes like mtgA would be crucial for understanding their role in antimicrobial resistance and developing targeted inhibitors.

• Experimental Approaches to Studying Enzyme Function:
  Recent methodological advances in experimental design for studying enzyme function, as outlined in the Dovetail guide to experimental research design, provide frameworks for investigating mtgA's role in antimicrobial resistance .

Researchers should monitor publications in journals focusing on antimicrobial resistance, bacterial pathogenesis, and structural biology for the most current information on Salmonella typhimurium mtgA.