Recombinant Citrobacter koseri Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is the function of monofunctional biosynthetic peptidoglycan transglycosylase (mtgA) in bacterial cell walls?

Monofunctional peptidoglycan glycosyltransferase (mtgA) specifically catalyzes glycan chain elongation of the bacterial cell wall. Unlike bifunctional peptidoglycan synthases such as PBP1a and PBP1b, mtgA possesses only glycosyltransferase activity, without transpeptidase activity. In bacterial species, mtgA functions by polymerizing lipid II precursors to form the glycan backbone of peptidoglycan . This process is essential for maintaining cell wall integrity and proper bacterial cell division. Experimental evidence has shown that GFP-MtgA fusion proteins demonstrate glycosyltransferase activity in vitro, with studies showing a 2.4-fold increase in peptidoglycan polymerization when GFP-MtgA is overexpressed compared to controls (26% versus 11% of lipid II used) .

How does mtgA contribute to bacterial cell division?

MtgA plays a significant role in bacterial cell division by participating in peptidoglycan synthesis at the division site. Localization studies have demonstrated that MtgA can localize at the division site of bacterial cells, particularly in cells deficient in certain penicillin-binding proteins (PBPs). MtgA interacts with multiple divisome proteins, including PBP3, FtsW, and FtsN, suggesting that it works collaboratively within the divisome to synthesize peptidoglycan during cell division . This coordination appears to be critical for proper septum formation and subsequent cell division. The interaction between mtgA and FtsN is particularly noteworthy, as FtsN has been shown to stimulate in vitro peptidoglycan synthesis activities and may coordinate peptidoglycan synthases during cell division .

What is known about the cellular localization of mtgA in bacteria?

MtgA localization in bacterial cells is influenced by the presence or absence of other peptidoglycan synthesis enzymes. In Escherichia coli cells deficient in PBP1b and producing a thermosensitive PBP1a, MtgA localizes specifically at the division site . When PBP1b is expressed from a plasmid in these cells, MtgA no longer localizes at the midcell, suggesting competitive localization between mtgA and class A PBPs . This conditional localization pattern indicates that mtgA might serve as a compensatory mechanism when primary peptidoglycan synthases are compromised. The specific localization pattern suggests that mtgA may play a backup role in peptidoglycan synthesis during cell division, particularly when other key enzymes are deficient or impaired.

What are the optimal methods for measuring recombinant C. koseri mtgA enzyme activity in vitro?

The activity of recombinant C. koseri mtgA can be effectively measured using radiolabeled substrates in an in vitro transglycosylase assay. Based on established protocols, the following methodology can be implemented:

Standard in vitro transglycosylase assay protocol:

• Reaction mixture composition:

  • Purified recombinant mtgA protein (5-10 μg)

  • C14-GlcNAc-labeled lipid II substrate (approximately 9,000-10,000 dpm/nmol)

  • 15% dimethyl sulfoxide

  • 10% octanol

  • 50 mM HEPES buffer (pH 7.0)

  • 0.5% decyl-polyethylene glycol

  • 10 mM CaCl₂

• Incubate the reaction at 30°C for 1 hour

• Separate the polymerized products using thin-layer chromatography

• Quantify the radiolabeled products using a phosphorimager or scintillation counting

• Calculate the percentage of lipid II substrate converted to polymer

Validation of polymerized products can be confirmed by adding lysozyme to the reaction products, which should result in complete digestion of the polymerized material . Activity is typically expressed as the percentage of lipid II substrate incorporated into polymerized peptidoglycan.

How can recombinant C. koseri mtgA be effectively expressed and purified for structural and functional studies?

Recommended expression and purification protocol:

• Vector construction:

  • Clone the C. koseri mtgA gene into a pET-based expression vector

  • Include a C-terminal His6-tag or other affinity tag for purification

  • Optional: create a fusion with GFP to monitor expression and purification

• Expression conditions:

  • Transform vector into E. coli BL21(DE3) or similar expression strain

  • Grow cells at 37°C to mid-log phase (OD600 = 0.6-0.8)

  • Induce protein expression with 0.5 mM IPTG

  • Reduce temperature to 18-20°C for overnight expression to enhance protein solubility

• Cell lysis and membrane protein extraction:

  • Harvest cells by centrifugation (5,000 × g, 15 min, 4°C)

  • Resuspend in lysis buffer containing 50 mM HEPES pH 7.5, 300 mM NaCl, 10% glycerol

  • Add protease inhibitors to prevent degradation

  • Disrupt cells by sonication or French press

  • Isolate membrane fraction by ultracentrifugation (100,000 × g, 1 h, 4°C)

  • Solubilize membrane proteins with appropriate detergent (e.g., 1% n-dodecyl-β-D-maltopyranoside)

• Purification steps:

  • Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin

  • Size exclusion chromatography to remove aggregates and obtain homogeneous protein

  • Concentrate purified protein to 5-10 mg/ml for enzymatic assays or crystallization

This protocol has been successful for similar membrane-associated glycosyltransferases and should yield functional recombinant mtgA protein with glycosyltransferase activity, as demonstrated by the successful purification of active GFP-MtgA fusion proteins described in previous research .

What approaches can be used to investigate mtgA protein-protein interactions within the divisome?

Several complementary approaches can be employed to study mtgA interactions with other divisome proteins:

1. Bacterial Two-Hybrid (BACTH) System:

• This system has been successfully used to detect interactions between mtgA and divisome proteins including PBP3, FtsW, and FtsN

• Advantages: Can detect interactions in a near-native environment

• Protocol highlights:

  • Clone mtgA and potential interaction partners into compatible BACTH vectors

  • Co-transform into reporter strain (e.g., E. coli BTH101)

  • Plate on selective media containing X-gal

  • Quantify interaction strength by β-galactosidase assay

2. Fluorescence Microscopy with Protein Localization:

• Create fluorescent protein fusions (e.g., GFP-mtgA) to visualize localization

• Perform co-localization studies with other fluorescently tagged divisome proteins

• Time-lapse microscopy can reveal the dynamics of protein recruitment to the division site

• Analyze co-localization using specialized software to generate quantitative data

3. Co-immunoprecipitation (Co-IP):

• Use antibodies against mtgA or tagged versions to pull down protein complexes

• Identify interaction partners by mass spectrometry

• Validate specific interactions by Western blotting

4. Surface Plasmon Resonance (SPR) or Isothermal Titration Calorimetry (ITC):

• For in vitro confirmation of direct interactions

• Determine binding affinities and thermodynamic parameters

• Requires purified proteins or protein domains

Data interpretation:
When analyzing protein-protein interactions, researchers should include appropriate controls and consider that membrane protein interactions may be affected by detergents used during purification. Cross-validation using multiple methods is recommended for confirming genuine interactions.

How does C. koseri mtgA compare with homologous enzymes in other pathogenic bacteria?

Comparative analysis of mtgA across different bacterial species reveals important insights into its conservation and potential specialization in C. koseri:

*Percentage identities are estimated based on typical sequence conservation patterns among Enterobacteriaceae

C. koseri possesses several unique genomic features that may influence mtgA function in the context of pathogenicity:

• The Group 8-specific core genome (C. koseri-specific) contains 285 gene families not found in other Citrobacter groups

• C. koseri has fewer antibiotic resistance genes compared to other Citrobacter species

• C. koseri contains specialized transport and metabolism genes that may provide advantages during infection

These genomic differences suggest that while mtgA's enzymatic function is likely conserved, its regulation, interaction partners, and contribution to pathogenicity may be specialized in C. koseri compared to homologs in other bacterial species.

How can CRISPR-Cas9 genome editing be optimized for studying mtgA function in C. koseri?

CRISPR-Cas9 genome editing can be a powerful approach to study mtgA function in C. koseri through gene knockout, modification, or complementation. Below is a methodological approach for implementing this technique:

Protocol for CRISPR-Cas9 editing of mtgA in C. koseri:

• sgRNA design:

  • Identify target sequences within the mtgA gene with minimal off-target effects

  • Design at least 3-4 different sgRNAs targeting different regions

  • Include NGG PAM sequences required for Cas9 recognition

  • Verify specificity using tools like CHOPCHOP or CRISPOR

• Vector construction:

  • Clone sgRNAs into a CRISPR-Cas9 vector compatible with C. koseri

  • For gene knockout: Design homology arms to introduce a stop codon or deletion

  • For gene modification: Design repair template with desired mutations flanked by homology arms

  • Include selectable marker for screening transformants

• Transformation of C. koseri:

  • Prepare electrocompetent C. koseri cells

  • Transform with CRISPR-Cas9 plasmid containing sgRNA and repair template

  • Recover cells in rich media before plating on selective media

  • Optimize electroporation parameters for C. koseri (typically 1.8-2.5 kV)

• Screening and validation:

  • Screen colonies by PCR to identify potential mutants

  • Confirm mutations by Sanger sequencing

  • Verify protein knockout by Western blot

  • Perform whole genome sequencing to check for off-target effects

• Phenotypic characterization:

  • Assess growth rates in different media conditions

  • Examine cell morphology by microscopy

  • Evaluate peptidoglycan composition by HPLC analysis

  • For pathogenicity studies: use animal models similar to those described for HPI mutant studies (e.g., 2-day-old SD rats and 18-day-old BALB/c mice)

Challenges and solutions:

• C. koseri may have lower transformation efficiency than model organisms

• Multiple chromosomal copies of the target gene may require sequential editing

• Potential effects on cell viability if mtgA is essential can be addressed by creating conditional mutants

This approach can be used to generate various mtgA mutants for functional studies, including complete gene knockouts, domain-specific mutations, or reporter gene fusions.

What in vitro reconstitution systems can be used to study the interaction between recombinant mtgA and other peptidoglycan synthesis enzymes?

In vitro reconstitution systems provide powerful tools for understanding the molecular mechanisms of mtgA activity and its interaction with other cell wall synthesis enzymes. The following methodological approaches can be employed:

1. Purified Protein Reconstitution System:

Components needed:

• Purified recombinant C. koseri mtgA (with or without fusion tags)

• Purified interacting proteins (PBP3, FtsW, FtsN)

• Lipid II substrate (radiolabeled or fluorescently labeled)

• Appropriate membrane mimetic environment (nanodiscs, liposomes, or detergent micelles)

Experimental approach:

• Reconstitute proteins individually or in combination in membrane mimetics

• Initiate reaction by adding lipid II substrate

• Monitor product formation using:

  • Thin-layer chromatography for radiolabeled substrates

  • HPLC analysis of digested products

  • Fluorescence-based assays for fluorescent substrates

• Compare activities of mtgA alone versus in combination with potential partner proteins

2. Fluorescence Resonance Energy Transfer (FRET) System:

This system allows real-time monitoring of protein-protein interactions and enzymatic activity:

• Label mtgA and interacting proteins with appropriate FRET pairs (e.g., CFP/YFP)

• Monitor FRET signal changes upon protein mixing and substrate addition

• Correlate FRET changes with enzymatic activity measurements

• Use this system to determine:

  • Binding kinetics between proteins

  • Conformational changes during catalysis

  • Effects of inhibitors or mutations

Sample data table that might be generated from such experiments:

These reconstitution systems allow detailed mechanistic studies of mtgA function and provide insights into how this enzyme coordinates with other divisome proteins during bacterial cell wall synthesis.

How should researchers interpret discrepancies between in vitro and in vivo studies of mtgA function?

When studying recombinant C. koseri mtgA, researchers often encounter differences between in vitro enzymatic assays and in vivo phenotypic observations. These discrepancies require careful interpretation:

Common discrepancies and interpretation strategies:

• Activity levels:

  • Observation: Purified recombinant mtgA shows lower activity in vitro than expected based on in vivo phenotypes

  • Interpretation approach: Consider that in vitro conditions may lack essential cofactors or interaction partners present in vivo

  • Solution: Supplement in vitro assays with factors like FtsN, which has been shown to stimulate peptidoglycan synthesis activities

• Localization patterns:

  • Observation: Differential localization of mtgA depending on genetic background

  • Interpretation approach: Analyze the presence/absence of competing enzymes (e.g., PBP1a, PBP1b)

  • Solution: Compare localization in wild-type vs. mutant backgrounds with defined genetic modifications

• Enzyme kinetics:

  • Observation: Substrate utilization rates differ between in vitro assays and cellular measurements

  • Interpretation approach: Consider substrate accessibility, local concentration effects, and membrane environment differences

  • Solution: Use membrane mimetics that better represent the native environment

• Protein-protein interactions:

  • Observation: Interactions detected in bacterial two-hybrid systems may not correlate with functional effects in vitro

  • Interpretation approach: Consider that interactions may be transient or dependent on specific cellular conditions

  • Solution: Validate interactions using multiple complementary methods and correlate with functional assays

When faced with discrepancies, researchers should:

• Systematically evaluate experimental conditions for both approaches

• Consider physiological relevance of buffer conditions, substrate concentrations, and reaction environments

• Develop more complex in vitro systems that better mimic cellular environments

• Use genetic approaches to validate biochemical findings and vice versa

What statistical approaches are most appropriate for analyzing mtgA enzymatic activity data?

Proper statistical analysis is crucial for interpreting enzymatic activity data for recombinant C. koseri mtgA. The following methodological approach is recommended:

Statistical workflow for mtgA activity data:

• Experimental design considerations:

  • Use biological replicates (minimum n=3) from independent protein preparations

  • Include technical replicates (typically 2-3) for each biological replicate

  • Include appropriate positive and negative controls in each experiment

• Data normalization approaches:

  • Normalize raw activity data to:

    • Protein concentration (specific activity)

    • Positive control reference enzyme

    • Internal standard

  • For comparative studies, express data as relative activity (% of wild-type or untreated control)

• Statistical tests for different experimental scenarios:

  Experimental Scenario	Recommended Statistical Test	Assumptions to Verify
  Comparing wild-type vs. mutant mtgA	Student's t-test (paired or unpaired)	Normal distribution, equal variance
  Comparing multiple mutants	One-way ANOVA with post-hoc tests (Tukey's or Dunnett's)	Normal distribution, equal variance
  Dose-response experiments	Non-linear regression (4-parameter logistic model)	Appropriate curve fit
  Enzyme kinetics data	Non-linear regression for Michaelis-Menten or allosteric models	Substrate ranges cover Km
  Non-normally distributed data	Non-parametric tests (Mann-Whitney, Kruskal-Wallis)	No specific distribution requirement

• Reporting standards:

  • Report means ± standard deviation or standard error

  • Include p-values for statistical comparisons

  • Provide n values (biological replicates)

  • Report confidence intervals where appropriate

  • Include raw data points in graphical representations

• Advanced analysis for complex datasets:

  • Principal component analysis for identifying patterns in multivariate data

  • Hierarchical clustering for identifying relationships between multiple mutants

  • Machine learning approaches for identifying structure-function relationships

For enzyme kinetic parameters, the following data presentation format is recommended:

By following these statistical approaches, researchers can ensure robust and reproducible analysis of mtgA enzymatic data, facilitating meaningful comparisons across different experimental conditions and between different research groups.

What are the most promising approaches for targeting mtgA in the development of novel antimicrobial strategies?

Based on current understanding of C. koseri mtgA and related transglycosylases, several promising research avenues for antimicrobial development can be pursued:

• Structure-based inhibitor design:

  • Determine high-resolution crystal structure of C. koseri mtgA

  • Identify unique structural features compared to human enzymes

  • Design selective inhibitors targeting the active site or allosteric sites

  • Focus on non-substrate analogs to avoid cross-resistance with existing glycopeptide antibiotics

• Combination therapy approaches:

  • Explore synergistic effects between mtgA inhibitors and:

    • β-lactam antibiotics targeting PBPs

    • Cell division inhibitors

    • Membrane-disrupting agents

  • Design dual-targeting molecules that simultaneously inhibit mtgA and other cell wall synthesis enzymes

• Protein-protein interaction disruptors:

  • Target the interactions between mtgA and divisome proteins (PBP3, FtsW, FtsN)

  • Develop peptide-based inhibitors mimicking interaction interfaces

  • Screen for small molecules that disrupt these essential protein complexes

• Pathogen-specific targeting:

  • Leverage C. koseri-specific features for selective targeting

  • Focus on differences in mtgA between C. koseri and commensal bacteria

  • Develop narrow-spectrum agents to minimize disruption of the microbiome

• Immunological approaches:

  • Investigate mtgA as a potential vaccine antigen

  • Develop antibodies targeting surface-exposed regions of mtgA

  • Explore the potential for antibody-antibiotic conjugates for targeted delivery

Research priorities table:

This multifaceted approach to targeting mtgA would create new opportunities for combating C. koseri infections, particularly in vulnerable populations such as neonates and immunocompromised individuals where this pathogen causes serious central nervous system infections .

How might systems biology approaches enhance our understanding of mtgA's role in C. koseri physiology and pathogenesis?

Systems biology approaches offer powerful frameworks for understanding mtgA function within the broader context of C. koseri cellular processes:

• Multi-omics integration strategies:

  • Combine transcriptomics, proteomics, and metabolomics data from mtgA mutants

  • Map changes across multiple cellular pathways

  • Identify compensatory mechanisms activated when mtgA function is compromised

  • Correlate with phenotypic changes in growth, morphology, and virulence

  Methodological approach:

  • Generate mtgA knockout or conditional mutants

  • Analyze global transcript, protein, and metabolite profiles under various conditions

  • Apply network analysis to identify key hubs and regulatory connections

  • Validate predictions through targeted experiments

• Computational modeling of cell wall biogenesis:

  • Develop mathematical models incorporating:

    • Enzymatic activities of all peptidoglycan synthesis enzymes

    • Spatial and temporal regulation during cell division

    • Interaction networks within the divisome

  • Simulate effects of mtgA perturbation on cell wall structure and integrity

  • Predict compensatory mechanisms and synthetic lethal interactions

• In vivo infection dynamics:

  • Track C. koseri proliferation and dissemination in animal models

  • Compare wild-type and mtgA mutant strains

  • Identify tissue-specific requirements for mtgA function

  • Correlate with host immune responses and pathological outcomes

  Experimental design elements:

  • Use fluorescently labeled bacterial strains for in vivo tracking

  • Employ tissue-specific RNA-seq to capture host-pathogen interactions

  • Analyze bacterial transcriptomes from infected tissues

  • Apply similar approaches used in HPI cluster studies that demonstrated attenuated virulence in animal models

• Synthetic biology approaches:

  • Engineer C. koseri strains with modified mtgA expression or activity

  • Introduce heterologous transglycosylases to assess functional complementation

  • Create chimeric enzymes to identify domain-specific functions

  • Develop inducible systems for temporal control of mtgA expression

Integrated data interpretation framework:

By integrating these systems biology approaches, researchers can develop a comprehensive understanding of how mtgA functions within the complex cellular network of C. koseri, potentially revealing new therapeutic targets and intervention strategies for treating infections caused by this important opportunistic pathogen.

What are the critical knowledge gaps that need to be addressed in C. koseri mtgA research?

Despite growing understanding of bacterial peptidoglycan synthesis, several critical knowledge gaps remain in our understanding of C. koseri mtgA:

• Structural characterization: The three-dimensional structure of C. koseri mtgA remains undetermined, limiting structure-based approaches to understanding its function and developing inhibitors.

• Regulatory mechanisms: How expression and activity of mtgA are regulated in response to environmental conditions, antibiotic stress, and during infection remains poorly understood.

• Functional redundancy: The extent to which other glycosyltransferases can compensate for mtgA deficiency in C. koseri needs further investigation, particularly given observations of conditional localization in E. coli .

• Species-specific differences: While some information can be extrapolated from E. coli studies, the specific properties and interactions of C. koseri mtgA require direct experimental validation.

• Contribution to pathogenesis: The specific role of mtgA in C. koseri virulence, particularly in the context of central nervous system infections, remains to be elucidated.

These knowledge gaps present significant opportunities for researchers to make important contributions to our understanding of C. koseri biology and pathogenesis.

What standardized protocols would benefit the research community studying bacterial transglycosylases?

To accelerate progress and enable meaningful comparisons across studies, the following standardized protocols would benefit researchers studying bacterial transglycosylases:

• Enzyme activity assays:

  • Standardized substrate preparation methods

  • Defined reaction conditions (buffer composition, pH, temperature)

  • Calibrated activity units and reporting standards

  • Reference enzyme standards for inter-laboratory comparisons

• Protein production and purification:

  • Optimized expression constructs and host systems

  • Detailed purification protocols for maintaining enzyme stability

  • Quality control standards for assessing protein purity and activity

  • Methods for reconstitution in membrane-mimetic environments

• Genetic manipulation:

  • Validated CRISPR-Cas9 protocols for C. koseri

  • Standardized knockout and complementation strategies

  • Reporter systems for monitoring gene expression

  • Phenotypic assay standards for mutant characterization

• Structural analysis:

  • Optimized crystallization conditions for transglycosylases

  • NMR protocols for studying dynamic regions

  • Cryo-EM approaches for membrane-associated enzyme complexes

  • In silico modeling validation standards

• Animal infection models:

  • Standardized protocols for rat and mouse models similar to those used in HPI studies

  • Defined bacterial inoculum preparation

  • Consistent sampling and analysis procedures

  • Ethical guidelines for minimizing animal use