Recombinant Shigella sonnei Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is Shigella sonnei Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA) and what is its role in bacterial cell wall synthesis?

Shigella sonnei mtgA (Monofunctional biosynthetic peptidoglycan transglycosylase) is a critical enzyme involved in bacterial cell wall biosynthesis. It functions as a glycan polymerase (peptidoglycan glycosyltransferase) that catalyzes the polymerization of glycan strands during peptidoglycan assembly . The protein contains 242 amino acids in its full-length form and includes a transmembrane (TM) domain that significantly influences its enzymatic activity . Unlike bifunctional penicillin-binding proteins (PBPs), mtgA performs only the transglycosylation reaction without having transpeptidase activity. This enzyme is essential for maintaining cell wall integrity in Shigella sonnei, a Gram-negative bacterium responsible for shigellosis, a significant cause of diarrheal illness globally .

How does the structure of mtgA protein relate to its function, particularly regarding the transmembrane domain?

The full-length mtgA protein (242 amino acids) contains a transmembrane (TM) domain that plays a crucial role in its enzymatic function. Research has demonstrated that the TM segment significantly enhances the glycosyltransferase activity compared to truncated forms lacking this domain . The amino acid sequence (MSKSRLTVFSFVRRFLLLLMVVLAVFWGGGIALFSVAPVPFSAVMVERQVSAWLHGNFRY VAHSDWVSMDQISPWMGLAVIAAEDQKFSEHWGFDVASIEKALAHNERNENRIRGASTIS QQTAKNLFLWDGRSWVRKGLEAGLTLGIETVWSKKRILTVYLNIAEFGDGVFGVEAAAQR YFHKPASKLTRSEAALLAAVLPNPLRFKVSSPSGYVRSRQAWILRQMYQLGGEPFMQQHQ LD) reveals hydrophobic regions consistent with membrane insertion .

Studies comparing full-length mtgA with truncated versions have shown that the TM domain influences substrate binding, interaction with moenomycin (a natural product inhibitor), and affects the length of glycan chains produced during the transglycosylase reaction . This structural-functional relationship is similar to observations in other peptidoglycan synthases like PBP1b, where the TM segment enhances enzymatic activity. The transmembrane domain likely facilitates proper orientation of the catalytic domain relative to the peptidoglycan substrate at the membrane interface, optimizing the polymerization reaction.

What expression systems are most effective for producing recombinant Shigella sonnei mtgA protein?

E. coli expression systems have proven effective for producing recombinant Shigella sonnei mtgA protein with high purity (>90% as determined by SDS-PAGE) . The full-length protein (1-242 amino acids) can be successfully expressed with an N-terminal His-tag to facilitate purification. This approach allows for convenient affinity chromatography purification while maintaining the protein's structural integrity.

When designing expression systems for mtgA, researchers should consider:

• Codon optimization for the expression host

• Selection of appropriate purification tags (His-tag positioning appears effective at the N-terminus)

• Expression conditions that prevent formation of inclusion bodies

• Purification strategies that maintain the transmembrane domain's integrity

The resulting lyophilized protein can be successfully reconstituted in deionized sterile water at concentrations of 0.1-1.0 mg/mL, with recommended addition of 5-50% glycerol for long-term storage stability .

How should experiments be designed to study mtgA function in the context of Shigella pathogenesis?

Proper experimental design is crucial when studying mtgA function in Shigella pathogenesis. A systematic approach should include:

Remember that experimental design fundamentally determines the types of biological inferences that can be drawn from your data .

What are the optimal storage and handling conditions for maintaining recombinant mtgA protein activity?

Based on available data, the following storage and handling protocols are recommended to maintain optimal recombinant mtgA activity:

Prior to reconstitution, the lyophilized protein powder should be briefly centrifuged to bring contents to the bottom of the vial . For experiments requiring active enzyme, it's important to verify activity after storage using appropriate enzymatic assays, as prolonged storage or improper handling may reduce catalytic efficiency.

What approaches can be used to measure mtgA enzymatic activity and inhibition?

Multiple complementary approaches can be employed to assess mtgA enzymatic activity and inhibition:

• Substrate-based assays:

  • Use peptidoglycan glycosyltransferase substrate mimics as templates for monitoring transglycosylase activity

  • Employ fluorescently-labeled lipid II substrates to track polymerization in real-time

  • Measure incorporation of radiolabeled substrates into polymerized peptidoglycan

• Inhibition studies:

  • Test known transglycosylase inhibitors (e.g., moenomycin) with defined IC50 values as positive controls

  • Establish dose-response relationships for potential inhibitors

  • Investigate the role of the transmembrane domain in inhibitor binding

• Structural approaches:

  • Employ binding assays to determine inhibitor affinity

  • Investigate structure-activity relationships of inhibitors

  • Consider how the transmembrane segment influences substrate and inhibitor interactions

• Cellular assays:

  • Measure impact on bacterial cell wall integrity

  • Assess synergy with other cell wall-targeting compounds

  • Quantify effects on Shigella growth and morphology

When designing these assays, ensure inclusion of appropriate controls and consider how the transmembrane domain influences activity, as full-length mtgA with intact TM segments demonstrates higher enzymatic activity than truncated forms .

How can genomic and structural variation in Shigella sonnei mtgA be analyzed across clinical isolates?

Analysis of genomic and structural variation in Shigella sonnei mtgA across clinical isolates requires a comprehensive approach combining various genomic technologies and analytical methods:

• Whole genome sequencing:

  • Use both short-read (e.g., Illumina) and long-read (e.g., PacBio) sequencing to capture both small mutations and larger structural variations

  • Implement appropriate sequence quality controls and coverage depth

  • Develop targeted sequencing approaches for the mtgA gene region

• Bioinformatic analysis pipeline:

  • Employ reference-based mapping to identify single nucleotide polymorphisms (SNPs)

  • Use de novo assembly to identify structural variations

  • Analyze mobile genetic elements that may impact mtgA expression

  • Examine variations in transmembrane domains that could affect function

• Population genetics approach:

  • Compare mtgA sequences across different lineages and outbreaks

  • Analyze temporal changes in genetic variation

  • Assess geographic distribution of variants

  • Correlate with antimicrobial resistance profiles

• Structure-function analysis:

  • Map genetic variations to protein structure

  • Predict functional impacts using computational methods

  • Validate predictions with biochemical assays

What are the methodological challenges in studying the role of mtgA in persistent Shigella infections and how can they be addressed?

Studying mtgA's role in persistent Shigella infections presents several methodological challenges:

• Clinical isolate collection and characterization:

  • Challenge: Obtaining paired isolates from persistent infections

  • Solution: Implement longitudinal sampling protocols with careful recordkeeping of patient metadata and establish biobanking workflows

• Distinguishing reinfection from persistence:

  • Challenge: Determining whether sequential isolates represent true persistence or reinfection

  • Solution: Apply whole genome sequencing and phylogenetic analyses to determine genetic relatedness between isolates

• In vitro modeling of persistence:

  • Challenge: Standard laboratory conditions may not reflect in vivo persistence environments

  • Solution: Develop specialized culture conditions mimicking host microenvironments and stress conditions

• Phenotypic characterization:

  • Challenge: Connecting mtgA sequence variants to functional differences

  • Solution: Develop robust enzymatic assays that can detect subtle functional variations

• Host-pathogen interactions:

  • Challenge: Understanding how mtgA activity influences immune recognition and evasion

  • Solution: Implement cell culture and animal models specifically designed to study persistence

Recent research has documented persistent Shigella infections in certain populations, including genomic changes during persistence . These studies have revealed acquisition or loss of antimicrobial resistance elements during persistent infections, highlighting the dynamic nature of Shigella genomes during host colonization. Similar approaches could be applied to understand how mtgA may evolve during persistent infections.

How does mtgA function intersect with antimicrobial resistance mechanisms in Shigella sonnei?

The intersection between mtgA function and antimicrobial resistance (AMR) in Shigella sonnei represents an important research area with several key considerations:

• Direct connections:

  • As a peptidoglycan synthesis enzyme, mtgA represents a potential antimicrobial target

  • Modifications in mtgA expression or structure could potentially alter susceptibility to cell wall-targeting antibiotics

  • The transmembrane domain of mtgA influences enzymatic activity and could impact how antibiotics interact with their targets

• Genomic context and horizontal gene transfer:

  • Recent studies have identified several mobile genetic elements carrying AMR genes in Shigella isolates

  • Key plasmids like pKSR100 (carrying azithromycin resistance genes mphA and ermB) have been documented in 43% of S. sonnei isolates

  • Research should investigate whether mtgA variants co-segregate with particular AMR determinants

• Research methodologies:

  • Combine genomic analyses with phenotypic susceptibility testing

  • Investigate whether alterations in cell wall synthesis via mtgA affect entry or activity of antibiotics

  • Examine transcriptional responses of mtgA to antibiotic exposure

  • Use genetic manipulation (knockouts, complementation) to directly test mtgA's role in AMR

• Population-level analyses:

  • Study mtgA sequence variation in the context of increasing antimicrobial resistance

  • Analyze whether specific mtgA variants are enriched in multidrug-resistant lineages

  • Consider how selection pressures from antibiotics might impact mtgA evolution

The rising prevalence of AMR in Shigella, including extended-spectrum beta-lactamase genes like blaSHV-12 , makes this research particularly relevant for understanding resistance mechanisms and developing novel therapeutics.

What are common challenges in purifying active recombinant mtgA protein and how can they be overcome?

Purification of active recombinant mtgA presents several challenges due to its transmembrane domain and enzymatic properties. Here are key issues and recommended solutions:

Successful purification has been reported using E. coli expression systems with N-terminal His-tags , allowing for efficient purification using immobilized metal affinity chromatography (IMAC). The purified protein can be obtained with >90% purity as determined by SDS-PAGE and stored as a lyophilized powder, which can then be reconstituted in deionized water to concentrations of 0.1-1.0 mg/mL .

How can researchers address data reproducibility challenges when studying mtgA enzymatic activity?

Ensuring reproducibility in mtgA enzymatic activity studies requires addressing several critical factors:

• Standardization of protein preparation:

  • Document detailed protocols for expression and purification

  • Characterize protein purity using multiple methods (SDS-PAGE, mass spectrometry)

  • Determine protein concentration using standardized methods

  • Establish quality control criteria for protein batches

• Assay consistency:

  • Define standard operating procedures for activity assays

  • Include internal controls for normalization between experiments

  • Validate equipment calibration and performance

  • Standardize substrate preparation and quality control

• Experimental design principles:

  • Ensure adequate biological and technical replication

  • Use randomization to minimize systematic bias

  • Consider power calculations when determining sample sizes

  • Document all methodological details completely

• Data analysis and reporting:

  • Pre-define analytical approaches before data collection

  • Use appropriate statistical methods based on data distribution

  • Account for batch effects in multi-experiment analyses

  • Report negative and inconclusive results alongside positive findings

• Data management:

  • Implement comprehensive data collection and storage systems

  • Document data provenance throughout analysis

  • Consider database structures that facilitate integration with other datasets

Applying these principles helps address what has been identified as "the greatest challenge of toxicogenomics": not data generation but effective collection, management, analysis, and interpretation of data . These same principles apply to enzymology studies with mtgA.

What computational approaches can be used to analyze mtgA structure-function relationships and predict activity of variants?

Advanced computational approaches can significantly enhance understanding of mtgA structure-function relationships:

• Structural modeling techniques:

  • Homology modeling based on related peptidoglycan glycosyltransferases

  • Molecular dynamics simulations to understand transmembrane domain contributions

  • Docking studies with substrates and inhibitors

  • Quantum mechanics/molecular mechanics (QM/MM) approaches for catalytic mechanism investigation

• Sequence-based analyses:

  • Multiple sequence alignment of mtgA across bacterial species

  • Conservation analysis to identify functionally important residues

  • Coevolutionary analysis to identify residue networks

  • Prediction of transmembrane topology and secondary structure

• Machine learning approaches:

  • Development of predictive models for variant activity

  • Feature extraction from sequence and structural data

  • Integration of experimental data to train supervised learning algorithms

  • Network-based approaches to understand system-level effects

• Data integration frameworks:

  • Combine genomic, structural, and functional data

  • Develop visualization tools for multi-dimensional data

  • Implement database structures that capture relationships between elements

  • Apply statistical methods to investigate associations

When developing computational approaches, researchers should follow the principle outlined for toxicogenomics: "creating such a database that captures relevant information would allow more extensive data mining and exploration and would provide opportunities currently not available" . This same concept applies to mtgA research, where integration of diverse data types can yield new insights into structure-function relationships.

What are the most promising future research directions for Shigella sonnei mtgA as a potential antimicrobial target?

The investigation of Shigella sonnei mtgA as an antimicrobial target presents several promising research directions:

• Structure-based drug design:

  • Leveraging structural information to design specific mtgA inhibitors

  • Exploring the unique features of the mtgA active site compared to related enzymes

  • Targeting protein-protein interactions or allosteric sites

  • Investigating how the transmembrane domain influences drug binding and efficacy

• Combination therapy approaches:

  • Exploring synergistic effects between mtgA inhibitors and existing antibiotics

  • Investigating whether mtgA inhibition sensitizes resistant Shigella to conventional antibiotics

  • Developing multi-target approaches addressing cell wall synthesis at multiple steps

• Innovative delivery systems:

  • Designing delivery methods to overcome bacterial membrane barriers

  • Developing prodrug approaches specific to Shigella infection sites

  • Exploring targeted delivery to reduce impacts on commensal bacteria

• Resistance mechanism prediction:

  • Applying evolutionary models to predict potential resistance mechanisms

  • Proactively designing inhibitors to address predicted resistance

  • Creating mtgA inhibitor combinations that reduce resistance development

• Population-specific considerations:

  • Addressing the rise of antimicrobial resistance in high-risk populations

  • Considering regional variations in Shigella prevalence and resistance patterns

  • Developing interventions suitable for resource-limited settings

These approaches must consider the changing epidemiology of shigellosis, including its emergence as a sexually transmissible infection among men who have sex with men and the associated rise in antimicrobial resistance . The dynamic nature of Shigella genomes, including horizontal gene transfer of resistance elements, highlights the need for novel antimicrobial approaches.

How can integrated datasets enhance our understanding of mtgA in the context of Shigella pathogenesis?

Integrated datasets can significantly enhance understanding of mtgA's role in Shigella pathogenesis through several mechanisms:

• Multi-omic data integration:

  • Combining genomics, transcriptomics, proteomics, and metabolomics data to build comprehensive models

  • Correlating mtgA genetic variations with expression levels and enzymatic activity

  • Mapping how mtgA functions within broader cellular networks

  • Identifying regulatory elements controlling mtgA expression

• Clinical-laboratory data connections:

  • Linking mtgA variants to clinical outcomes and antimicrobial susceptibility

  • Correlating specific mutations with virulence or persistence phenotypes

  • Establishing connections between in vitro findings and in vivo pathogenesis

  • Tracking evolution of mtgA during persistent infections

• Database development principles:

  • Moving beyond individual datasets to create structured databases

  • Capturing relationships between experimental elements

  • Including metadata about experimental design and conditions

  • Facilitating data mining and exploration

• Analytical capabilities:

  • Developing new analytical methods for integrated data

  • Creating software tools accessible to research and regulatory communities

  • Applying machine learning approaches to complex datasets

  • Identifying patterns not apparent in single-domain analyses

As noted in research on experimental design, "clearly, a carefully designed database containing toxicogenomic data along with other information (such as structure-activity relationships and information about dose-response and phenotypic outcome for exposure) would allow many of the unanswered questions... to be addressed" . This same principle applies to mtgA research, where integrated datasets could reveal new insights into its role in Shigella pathogenesis.