Recombinant Pseudomonas syringae pv. phaseolicola Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is mtgA and what is its role in Pseudomonas syringae?

Monofunctional transglycosylase A (mtgA) is an enzyme involved in bacterial cell wall biosynthesis. In Pseudomonas syringae, mtgA functions as a dispensable monofunctional transglycosylase that plays an auxiliary role in peptidoglycan synthesis. The enzyme specifically catalyzes the polymerization of lipid II molecules into glycan strands of peptidoglycans, which are essential components of the bacterial cell wall . Unlike bifunctional penicillin-binding proteins that possess both transglycosylase and transpeptidase activities, mtgA exclusively performs the transglycosylase function, making it a unique target for studying specific aspects of cell wall assembly in this plant pathogen.

How does peptidoglycan synthesis contribute to Pseudomonas syringae pathogenicity?

Peptidoglycan synthesis plays a multifaceted role in Pseudomonas syringae pathogenicity through several mechanisms:

• Structural integrity: Properly synthesized peptidoglycan provides the structural framework necessary for bacterial survival during infection and colonization of plant tissues.

• PAMP recognition: Peptidoglycan fragments serve as pathogen-associated molecular patterns (PAMPs) that can trigger plant immune responses. The plant's pattern recognition receptors (PRRs) can detect these peptidoglycan fragments, initiating PAMP-triggered immunity (PTI) .

• Type III secretion system support: The peptidoglycan layer provides structural support for the type III secretion system, which is essential for the delivery of type III effector (T3E) proteins into host cells. These T3Es are primary virulence determinants that suppress host immunity and promote bacterial growth in planta .

• Cell morphology regulation: Proper peptidoglycan synthesis ensures appropriate bacterial cell shape and size, which may influence bacterial adaptation to different plant microenvironments during infection.

What distinguishes monofunctional transglycosylases from bifunctional peptidoglycan-synthesizing enzymes?

A comprehensive comparison of monofunctional transglycosylases like mtgA versus bifunctional enzymes reveals significant differences in structure, function, and research applications:

How should researchers design experiments to study mtgA function in Pseudomonas syringae?

When designing experiments to investigate mtgA function in Pseudomonas syringae, researchers should implement a systematic approach that addresses multiple aspects of the protein's biology:

• Gene disruption strategy: Create a clean deletion mutant of mtgA using allelic exchange techniques rather than insertional inactivation to avoid polar effects on neighboring genes. Include complementation controls by reintroducing the wild-type mtgA gene on a plasmid to verify phenotypic restoration.

• Phenotypic characterization: Examine multiple phenotypic parameters, including:

  • Growth kinetics in different media and conditions

  • Cell morphology through microscopy (both light and electron)

  • Peptidoglycan composition analysis via HPLC or mass spectrometry

  • Virulence assays on appropriate plant hosts

• Experimental variables: Test the mtgA mutant under multiple conditions that might stress the cell wall:

  • Osmotic stress conditions

  • Various growth temperatures

  • Different plant host environments

  • Presence of cell wall-targeting antimicrobials

• Statistical design: Plan for at least three biological replicates and three technical replicates for each experiment, ensuring sufficient statistical power to detect subtle phenotypic differences3. For time-course experiments, select appropriate intervals to capture the dynamics of the process being studied.

• Controls: Include appropriate controls in each experiment:

  • Wild-type strain

  • Complemented mtgA mutant

  • Known cell wall mutants with characterized phenotypes for comparison

  • Empty vector controls for complementation studies

What protocols are recommended for producing and purifying recombinant mtgA from Pseudomonas syringae?

A methodological approach to producing recombinant mtgA from Pseudomonas syringae involves several critical steps:

• Expression system selection:

  • For structural studies: Use E. coli BL21(DE3) with pET-based vectors for high yield

  • For functional studies: Consider Pseudomonas-based expression systems for proper folding and post-translational modifications

• Gene optimization and construct design:

  • Optimize codon usage for the expression host

  • Include a cleavable N-terminal or C-terminal affinity tag (His6 or GST)

  • Consider removing the transmembrane domain for improved solubility

  • Include a TEV protease cleavage site between the tag and mtgA

• Expression optimization:

  • Test multiple induction conditions (temperature, IPTG concentration, induction time)

  • Screen for solubility in different buffer systems

  • Consider membrane-mimicking environments if the protein associates with membranes

• Purification workflow:

  • Initial capture: Immobilized metal affinity chromatography (IMAC)

  • Secondary purification: Ion exchange chromatography

  • Final polishing: Size exclusion chromatography

  • For membrane-associated forms: Include detergent screening (DDM, LDAO, etc.)

• Quality control assessment:

  • SDS-PAGE for purity evaluation

  • Mass spectrometry for identity confirmation

  • Enzymatic activity assays to verify functional integrity

  • Circular dichroism to assess secondary structure

How can researchers effectively organize and present data from mtgA functional studies?

Effective data organization and presentation in mtgA research studies requires a structured approach to ensure clarity and reproducibility:

• Data table construction:
  When organizing experimental data, create tables that clearly present independent variables, dependent variables, and number of replicates3. For example:

  Treatment Condition	Cell Diameter (μm)	Cell Length (μm)	Peptidoglycan Thickness (nm)	Growth Rate (OD600/hr)
  Wild-type	0.76 ± 0.05	2.12 ± 0.11	6.3 ± 0.4	0.42 ± 0.03
  ΔmtgA	1.07 ± 0.08	2.09 ± 0.13	5.1 ± 0.6	0.36 ± 0.04
  ΔmtgA + pMtgA	0.78 ± 0.06	2.14 ± 0.09	6.1 ± 0.5	0.41 ± 0.02

• Graphical representation:

  • Select appropriate graph types based on data characteristics (bar graphs for comparisons, line graphs for time courses)

  • Include error bars representing standard deviation or standard error

  • Use consistent color schemes across related experiments

  • Provide clear legends that explain all symbols and colors3

• Statistical analysis:

  • Clearly state statistical tests used (t-test, ANOVA, etc.)

  • Report p-values and significance thresholds

  • Include sample sizes (n) for all experiments

  • Consider showing data distribution when appropriate (box plots, violin plots)

• Image data presentation:

  • Include scale bars on all microscopy images

  • Provide representative images alongside quantification

  • Use consistent magnification and imaging conditions when comparing strains

  • Present images in panels with clear labeling

How does mtgA deletion affect bacterial cell morphology and physiology?

The deletion of mtgA has been shown to induce significant morphological and physiological changes in bacterial cells under specific conditions. In E. coli, mtgA deletion leads to a remarkable increase in cell diameter without affecting cell length when the bacteria are producing polymers like P(LA-co-3HB), creating what researchers have termed "fat cells" . This phenotype is specifically associated with polymer accumulation, as mtgA-deleted strains show normal morphology under non-polymer-producing conditions.

The physiological effects of mtgA deletion include:

• Altered peptidoglycan architecture: The absence of mtgA likely changes the cross-linking pattern and glycan strand length in the peptidoglycan mesh, potentially creating a more flexible cell wall that can accommodate increased cellular volume.

• Enhanced polymer production: mtgA-deleted strains demonstrate increased polymer accumulation (approximately 34% higher than wild-type strains) , suggesting that changes in cell wall structure may influence metabolic processes related to polymer synthesis or storage.

• Complementation effects: The phenotypic changes observed in mtgA-deleted strains can be restored to wild-type characteristics through genetic complementation, confirming that these effects are directly attributable to mtgA function .

• Stress response alterations: mtgA-deleted strains may exhibit different responses to cell wall stressors compared to wild-type strains, potentially showing increased sensitivity to osmotic stress but potentially enhanced resistance to certain antimicrobials that target peptidoglycan synthesis.

What is the relationship between mtgA activity and type III effector delivery in Pseudomonas syringae?

The relationship between mtgA activity and type III effector delivery in Pseudomonas syringae represents a complex intersection of cell wall integrity and virulence mechanisms. While direct experimental evidence linking mtgA to type III secretion system (T3SS) function remains limited, several hypothetical connections can be proposed based on current knowledge:

• Structural foundation: The peptidoglycan layer provides a structural foundation for the T3SS apparatus, which spans both bacterial membranes and the cell wall. Alterations in peptidoglycan structure due to mtgA deletion could potentially affect T3SS assembly or stability, indirectly influencing effector delivery.

• Cell wall remodeling: T3SS assembly requires localized remodeling of the peptidoglycan layer to accommodate the secretion apparatus. mtgA might participate in this remodeling process, potentially in conjunction with lytic transglycosylases that create gaps in the peptidoglycan mesh.

• Regulatory crosstalk: Cell wall integrity sensing mechanisms may influence T3SS gene expression. If mtgA deletion activates cell wall stress responses, these could potentially modulate T3SS expression through complex regulatory networks.

• Effector translocation efficiency: Changes in cell wall architecture resulting from mtgA deletion might alter the efficiency of effector protein translocation through the T3SS, potentially affecting the timing or quantity of effector delivery during infection.

• Host immune signaling: Peptidoglycan fragments released during normal cell wall turnover can act as PAMPs, triggering plant immune responses . Alterations in peptidoglycan structure and turnover due to mtgA deletion might influence the profile of peptidoglycan fragments released, potentially affecting plant immune signaling.

How can researchers address contradictions in mtgA functional studies across different bacterial species?

Addressing contradictions in mtgA functional studies across different bacterial species requires a systematic approach to identify the sources of discrepancies and develop a coherent understanding of mtgA function in different contexts. Researchers should:

• Implement time-aware contradiction detection frameworks:

  • Develop a structured approach to identify when contradictory results emerge in the literature

  • Use atomic fact decomposition to pinpoint specific contradictory claims about mtgA function

  • Create explicit timelines for different experimental findings to determine if apparent contradictions might reflect temporal changes in mtgA activity

• Analyze methodological differences:

  • Systematically compare experimental conditions between contradictory studies

  • Evaluate the genetic background of bacterial strains used in different studies

  • Assess differences in protein expression systems, purification methods, and activity assays

• Consider species-specific factors:

  • Compare mtgA protein sequences across bacterial species to identify structural differences

  • Analyze the genomic context of mtgA in different species to identify potential functional partners

  • Evaluate the presence of functional redundancy with other transglycosylases in each species

• Design reconciliation experiments:

  • Perform cross-species complementation studies (e.g., express P. syringae mtgA in E. coli mtgA mutants)

  • Create chimeric mtgA proteins to identify domains responsible for species-specific functions

  • Test mtgA function under identical conditions across multiple bacterial species

• Implement statistical approaches for meta-analysis:

  • Conduct formal meta-analyses of quantitative data from multiple studies

  • Apply Bayesian methods to update confidence in specific hypotheses as new data emerges

  • Use statistical modeling to identify factors that best explain inter-study variability

What statistical approaches are most appropriate for analyzing mtgA mutant phenotypes?

Selecting appropriate statistical approaches for analyzing mtgA mutant phenotypes requires careful consideration of experimental design, data distribution, and the specific hypotheses being tested:

• For morphological comparisons (cell size, shape):

  • Analysis of Variance (ANOVA) followed by post-hoc tests (Tukey's HSD) for comparing multiple strains

  • Linear mixed-effects models when accounting for batch effects or repeated measurements

  • Image analysis validation using multiple measurement methods and blind scoring

• For growth and fitness analyses:

  • Growth curve fitting using non-linear regression models (Gompertz, logistic, etc.)

  • Area under the curve (AUC) analyses for integrated growth measurements

  • Survival analysis approaches for competitive fitness assays

• For polymer production and accumulation:

  • Multiple regression analysis to identify relationships between cell morphology and polymer production

  • Principal component analysis to reduce dimensionality when measuring multiple polymer characteristics

  • Bootstrapping methods for robust confidence interval estimation

• For time-series experiments:

  • Repeated measures ANOVA or linear mixed models for balanced designs

  • Generalized additive mixed models (GAMMs) for flexible modeling of non-linear time trends

  • Change-point analysis to identify significant transitions in temporal profiles

• For contradiction resolution:

  • Meta-analytical approaches to synthesize results across multiple studies

  • Bayesian hierarchical modeling to incorporate prior knowledge and update beliefs with new data

  • Sensitivity analyses to identify which experimental factors most strongly influence outcomes

How can timeline-based approaches help resolve contradictions in mtgA research findings?

Timeline-based approaches offer powerful frameworks for resolving apparent contradictions in mtgA research findings by explicitly modeling temporal relationships between events, states, and experimental observations :

• Atomic fact decomposition:

  • Break complex findings about mtgA function into atomic facts (e.g., "mtgA deletion increases cell diameter during polymer accumulation")

  • Assign validity intervals to each atomic fact based on experimental conditions

  • Identify contradictions by detecting temporal overlaps between incompatible atomic facts

• Pre-fact and post-fact analysis:

  • For each experimental intervention (e.g., mtgA deletion), define pre-facts (conditions before intervention) and post-facts (outcomes after intervention)

  • Use this structure to track state changes over time and detect contradictions when post-facts conflict with established knowledge

  • Apply formal contradiction detection algorithms to identify specific pairs of atomic facts in conflict

• Validity interval tracking:

  • Maintain a comprehensive world state model that tracks the validity intervals of all known facts about mtgA

  • Update validity intervals when new evidence emerges

  • Flag potential contradictions when facts with overlapping validity intervals make incompatible claims

• Implementation of FACTTRACK methodology:

  • Adopt a structured Decompose-Determine-Contradiction-Update pipeline for evaluating new research findings

  • Use natural language inference models to calculate contradiction scores between pairs of facts

  • Apply different thresholds for update conditions versus contradiction conditions to balance sensitivity and specificity

• Visualization tools for contradiction detection:

  • Develop timeline visualizations that explicitly show temporal relationships between findings

  • Create network diagrams that represent dependencies between atomic facts

  • Implement heat maps that highlight potential contradiction hotspots in the literature

What experimental controls are essential when studying compensatory mechanisms in mtgA mutants?

When investigating compensatory mechanisms in mtgA mutants, implementing robust controls is crucial for distinguishing true compensation from experimental artifacts:

• Genetic controls:

  • Clean deletion mutant with verified sequence: Ensure the mtgA gene has been completely removed without affecting flanking genes

  • Complementation strain: Reintroduce the wild-type mtgA gene on a plasmid to verify phenotype restoration

  • Empty vector control: Include the same plasmid backbone without mtgA to control for vector effects

  • Conditional expression system: Use inducible promoters to modulate mtgA expression levels

• Phenotypic baseline controls:

  • Wild-type reference measurements: Establish clear baseline values for all phenotypes under identical conditions

  • Known cell wall mutants: Include mutants with well-characterized cell wall defects for comparison

  • Growth condition matrix: Test multiple media compositions and growth conditions to identify condition-specific effects

• Temporal controls:

  • Time-course experiments: Monitor phenotypic changes over time to distinguish immediate from adaptive responses

  • Growth phase standardization: Compare strains at equivalent growth phases rather than absolute time points

  • Adaptation controls: Passage mutants through multiple generations to identify stable versus transient compensatory mechanisms

• Molecular controls for compensatory mechanism identification:

  • Transcriptomic analysis with spike-in controls: Include external RNA standards to enable absolute quantification

  • Proteomics with labeled reference proteins: Use SILAC or TMT approaches for accurate protein quantification

  • Metabolite analysis with internal standards: Include isotope-labeled standards for metabolomic analyses

• Validation controls:

  • Secondary mutation verification: Sequence genomes of adapted mtgA mutants to identify potential compensatory mutations

  • Reconstructed double mutants: Recreate identified compensatory mutations in clean genetic backgrounds

  • Cross-species validation: Test if identified compensatory mechanisms operate similarly in related bacterial species

What emerging techniques could advance our understanding of mtgA's role in bacterial cell wall synthesis?

Several cutting-edge techniques offer promising avenues for deeper insights into mtgA function:

• Cryo-electron tomography approaches:

  • In situ visualization of peptidoglycan synthesis complexes in native cellular environments

  • Direct observation of mtgA localization and interaction with other cell wall synthesis machinery

  • Structural characterization of peptidoglycan architecture in mtgA mutants versus wild-type cells

• Single-molecule tracking technologies:

  • Real-time tracking of fluorescently labeled mtgA molecules in living cells

  • Measurement of mtgA diffusion dynamics and residence times at sites of active peptidoglycan synthesis

  • Determination of stoichiometry and turnover rates of mtgA in synthetic complexes

• Advanced genetic approaches:

  • CRISPR interference (CRISPRi) for tunable repression of mtgA expression

  • Proximity-dependent protein labeling (BioID, APEX) to identify novel mtgA interaction partners

  • Deep mutational scanning to create comprehensive maps of structure-function relationships in mtgA

• Chemical biology tools:

  • Activity-based protein profiling using transglycosylase-specific probes

  • Photocrosslinking approaches to capture transient enzyme-substrate complexes

  • Bio-orthogonal click chemistry for selective labeling of newly synthesized peptidoglycan

• Computational approaches:

  • Molecular dynamics simulations of mtgA-substrate interactions

  • Machine learning algorithms to predict phenotypic consequences of mtgA mutations

  • Systems biology modeling of peptidoglycan synthesis networks

How might comparative studies across plant pathogen species inform our understanding of mtgA evolution?

Comparative studies of mtgA across diverse plant pathogen species could reveal evolutionary patterns and functional adaptations:

• Phylogenetic analyses:

  • Construct comprehensive phylogenetic trees of mtgA sequences across bacterial phyla

  • Identify signatures of selection on mtgA in plant pathogens versus non-pathogenic relatives

  • Map functional diversification of mtgA in relation to host range expansion

• Structural comparative analyses:

  • Compare three-dimensional structures of mtgA proteins from diverse pathogens

  • Identify conserved catalytic regions versus variable domains that might reflect host adaptation

  • Model substrate binding sites to detect potential specializations for different peptidoglycan chemotypes

• Functional complementation experiments:

  • Test cross-species complementation of mtgA mutants with orthologs from diverse pathogens

  • Identify host-specific functional requirements through heterologous expression

  • Create chimeric mtgA proteins to map domains responsible for species-specific functions

• Ecological contextual analyses:

  • Correlate mtgA sequence variations with pathogen lifestyle (biotrophic, hemibiotrophic, necrotrophic)

  • Examine mtgA evolution in relation to host plant cell wall composition

  • Investigate horizontal gene transfer events involving mtgA and their impact on pathogen evolution

• Co-evolutionary studies:

  • Analyze co-evolution of mtgA with other cell wall synthesis enzymes

  • Investigate potential co-evolution of mtgA with host plant pattern recognition receptors

  • Examine evolutionary trajectories of mtgA in relation to antibiotic resistance development

What potential applications might emerge from a deeper understanding of mtgA function in plant pathogens?

Advanced understanding of mtgA function could translate into several practical applications:

• Novel antimicrobial development strategies:

  • Design of specific transglycosylase inhibitors based on mtgA structure

  • Development of combination therapies targeting multiple steps in peptidoglycan synthesis

  • Creation of prodrugs activated by pathogen-specific peptidoglycan remodeling

• Engineered probiotics and biocontrol agents:

  • Creation of attenuated plant probiotics through targeted mtgA modifications

  • Development of engineered bacteria with altered cell wall properties for improved rhizosphere colonization

  • Design of sensor bacteria that detect and respond to pathogen-derived peptidoglycan fragments

• Plant immunity modulation approaches:

  • Engineering of plants with enhanced detection capabilities for specific peptidoglycan fragments

  • Development of peptidoglycan-based immune elicitors for crop protection

  • Design of strategies to circumvent pathogen suppression of peptidoglycan-triggered immunity

• Biotechnological applications:

  • Engineering of bacterial cell factories with enhanced polymer production through mtgA modification

  • Development of bacteria with designer cell morphologies for specialized applications

  • Creation of self-assembling bacterial materials with controlled cell wall properties

• Diagnostic applications:

  • Development of sensors for detecting specific peptidoglycan fragments as disease biomarkers

  • Creation of diagnostic assays based on species-specific mtgA properties

  • Design of imaging agents targeting peptidoglycan synthesis for tracking bacterial infections in planta