Recombinant Salmonella dublin Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is the genomic context of the mtgA gene in Salmonella dublin?

The mtgA gene in Salmonella dublin encodes the monofunctional biosynthetic peptidoglycan transglycosylase enzyme that catalyzes glycosidic bond formation during peptidoglycan synthesis. While the search results don't specifically address the genomic context of mtgA, they do provide insight into the genomic architecture of Salmonella dublin, particularly regarding antimicrobial resistance determinants. Several S. dublin isolates have been sequenced and analyzed for genomic islands and mobile genetic elements. For instance, multiple S. dublin isolates were positive for ISVsa3, a mobile genetic element associated with multidrug resistance . When designing experiments to study mtgA, researchers should consider its genomic context relative to these resistance elements, especially if studying clinical isolates with varying resistance profiles.

How does Salmonella dublin mtgA differ from homologous enzymes in other Salmonella serotypes?

Although the search results don't directly compare mtgA across Salmonella serotypes, they do illustrate serotype-specific patterns in antimicrobial resistance genes that may indirectly affect cell wall synthesis pathways. Salmonella dublin appears to have distinctive patterns of antimicrobial resistance compared to other serotypes. For example, in the dataset provided, multiple S. dublin isolates (RM043, RM052, RM079, RM092, RM094, RM099, RM100, RM104, RM105, and RM111) exhibited resistance to florfenicol (FFN), oxytetracycline (OXY), and ceftiofur (XNL) . To study differences in mtgA across serotypes, researchers should perform comparative genomics analyses focusing on SNPs, indels, and upstream regulatory regions that might affect expression or protein structure. Additionally, recombinant expression and enzymatic characterization would help establish functional differences.

What are the optimal conditions for expressing recombinant Salmonella dublin mtgA in E. coli?

For successful expression of recombinant S. dublin mtgA in E. coli, researchers should consider the following methodological approach: (1) Codon optimization for E. coli expression, particularly if the GC content differs significantly from E. coli; (2) Selection of an appropriate expression vector with an inducible promoter (e.g., pET system with T7 promoter); (3) Fusion tags for purification and solubility enhancement (His6-tag, MBP, or SUMO); (4) Expression in specialized E. coli strains that provide rare tRNAs or enhance disulfide bond formation if needed; (5) Optimization of induction conditions (temperature, IPTG concentration, induction time). Typically, lowering the induction temperature to 16-20°C and reducing IPTG concentration can improve soluble protein yield for membrane-associated enzymes like mtgA. Post-expression analysis should include SDS-PAGE, Western blotting, and activity assays to confirm successful expression of functional protein.

How does antimicrobial resistance in Salmonella dublin affect mtgA function and peptidoglycan synthesis?

Antimicrobial resistance in S. dublin, particularly to cephalosporins like ceftiofur (XNL), may indirectly impact mtgA function and peptidoglycan synthesis. According to the search results, multiple S. dublin isolates carried the blaCMY-2 gene, which encodes a β-lactamase that confers resistance to β-lactam antibiotics that target peptidoglycan synthesis . While mtgA itself is not a direct target of these antibiotics, altered peptidoglycan structure or composition resulting from resistance mechanisms may affect mtgA activity or regulation.

To investigate this relationship, researchers should design experiments that:

• Compare peptidoglycan composition in resistant versus susceptible S. dublin strains

• Analyze mtgA expression levels in response to β-lactam antibiotics

• Assess peptidoglycan cross-linking and cell wall thickness in resistant strains

• Perform in vitro assays with purified mtgA using peptidoglycan precursors from resistant and susceptible strains

The table below shows antimicrobial resistance patterns in S. dublin isolates that could be used for such comparative studies:

What structural features of Salmonella dublin mtgA are critical for enzymatic activity and potential inhibitor design?

While the search results don't provide specific structural information about S. dublin mtgA, researchers investigating this question should employ a systematic approach combining computational modeling and experimental validation. Begin with homology modeling based on crystal structures of related transglycosylases from other bacteria. Key structural features to analyze include:

• The catalytic domain containing conserved glutamate residues essential for glycosyltransferase activity

• Substrate binding sites that recognize lipid II

• Transmembrane or membrane-associated domains

• Potential allosteric sites

For experimental validation of these structural predictions, researchers should use site-directed mutagenesis to modify predicted key residues, followed by enzymatic assays to measure transglycosylase activity. Circular dichroism spectroscopy and thermal shift assays can assess structural integrity of mutants. For inhibitor design, molecular docking studies targeting the active site or allosteric sites should be conducted, followed by in vitro testing of candidate inhibitors using purified recombinant mtgA in transglycosylase activity assays.

How does ISVsa3-associated multidrug resistance influence experimental approaches to studying recombinant Salmonella dublin mtgA?

The strong association between ISVsa3 and multidrug resistance in Salmonella isolates has significant implications for experimental design when studying mtgA . According to the search results, 93.9% of multidrug-resistant isolates were positive for ISVsa3, compared to only 7.7% of non-MDR isolates (OR = 186.00, p < 0.0001) . This mobile genetic element is frequently associated with a constellation of resistance genes (floR, tet(A), aph(6)-Id, aph(3′′)-Ib, and sul2) that may be carried on IncC type plasmids .

Researchers should consider the following methodological adjustments:

• Strain selection: Choose S. dublin strains with well-characterized antimicrobial resistance profiles. The table below summarizes key data on AMR genes associated with ISVsa3:

• Genetic engineering: When creating recombinant systems, consider whether mtgA expression or function might be influenced by the presence of ISVsa3 or associated resistance determinants. Construct isogenic strains differing only in ISVsa3 status to isolate this effect.

• Culture conditions: MDR strains may respond differently to growth conditions, potentially affecting mtgA expression or activity. Optimize growth media and conditions for each strain type.

• Antibiotic selection: For plasmid maintenance in recombinant systems, carefully select antibiotics that won't interfere with the strain's resistance profile or potentially influence cell wall synthesis pathways.

• Genomic context analysis: Perform whole genome sequencing to determine the precise genomic location of mtgA relative to ISVsa3 and resistance genes, which may reveal potential regulatory interactions.

What experimental controls are essential when assessing enzymatic activity of recombinant Salmonella dublin mtgA?

When assessing the enzymatic activity of recombinant S. dublin mtgA, researchers must implement rigorous controls to ensure reliable and interpretable results. A comprehensive control strategy should include:

• Negative enzymatic controls:

  • Heat-inactivated mtgA enzyme

  • Catalytically inactive mtgA mutant (mutation in the conserved catalytic glutamate)

  • Reaction buffer without enzyme

• Positive controls:

  • Commercial transglycosylase with known activity

  • Well-characterized mtgA from a related organism (e.g., E. coli)

• Substrate controls:

  • Varying concentrations of lipid II substrate to establish Michaelis-Menten kinetics

  • Chemically modified substrates to verify specificity

• Assay validation controls:

  • Measure activity using multiple independent methods (e.g., fluorescence-based assay, HPLC detection of products)

  • Internal standards for quantitative assays

• Biological context controls:

  • Compare activity of mtgA from antimicrobial-resistant versus susceptible S. dublin strains

  • Assess activity in the presence of cell wall fragments from S. dublin strains with different resistance profiles

The search results indicate that S. dublin isolates exhibit various antimicrobial resistance patterns , which may reflect differences in cell wall composition or metabolism. Researchers should consider how these differences might influence mtgA activity and incorporate appropriate biological controls from well-characterized resistant and susceptible strains.

How can quasi-experimental designs be applied to study the impact of mtgA mutations on Salmonella dublin virulence?

Quasi-experimental designs offer valuable approaches for studying the impact of mtgA mutations on S. dublin virulence when randomized experiments are not feasible. Based on search result3, which discusses strengths and weaknesses of experimental and quasi-experimental designs, researchers can implement the following methodological strategies:

• Regression Discontinuity Design:

  • Create a continuous scoring system for mtgA sequence variations

  • Establish a threshold that separates "high mutation" from "low mutation" strains

  • Compare virulence outcomes on either side of this threshold

  • This approach is particularly valuable as it "estimates the effect of treatment at the cutoff rather than the average effect of treatment"3

• Interrupted Time Series Design:

  • Measure virulence parameters before and after introducing specific mtgA mutations

  • Conduct multiple measurements over time to establish trends

  • Analyze whether the introduction of mutations changes the trajectory of virulence measures

• Matching Procedures:

  • Implement the hybrid matching procedure described in search result3

  • Match strains with mtgA mutations to wild-type controls based on both "local" factors (isolation source, date) and "focal" characteristics (genetic background, other virulence factors)

  • This approach showed superior results in the example from search result3, reducing differences from approximately "a third of a standard deviation" to "virtually zero" for one outcome measure3

• Statistical Controls:

  • Use pretest measures of virulence factors as covariates in statistical models

  • Apply multiple regression or propensity score methods to adjust for confounding variables

When applying these quasi-experimental designs, researchers should ensure "minimal confounds between the randomized experiment and the quasi-experiment other than assignment itself"3 and establish clear "criteria for judging whether the two results are the same"3.

What are the most effective purification strategies for obtaining high-yield, active recombinant Salmonella dublin mtgA?

Purifying active recombinant S. dublin mtgA presents specific challenges due to its membrane association and complex enzymatic activity. Based on established methodologies for similar bacterial glycosyltransferases, researchers should implement a systematic purification strategy:

• Expression optimization:

  • Use E. coli BL21(DE3) or Rosetta(DE3) strains to accommodate potential rare codons

  • Test multiple fusion tags (His6, MBP, SUMO) to identify optimal solubility

  • Express at low temperature (16-18°C) with reduced IPTG concentration (0.1-0.5 mM)

• Cell lysis and membrane fraction preparation:

  • Use gentle lysis methods (e.g., lysozyme treatment followed by sonication)

  • Separate membrane fraction by ultracentrifugation (100,000 × g for 1 hour)

  • Extract membrane proteins using mild detergents (n-dodecyl-β-D-maltoside or CHAPS)

• Chromatography sequence:

  • Immobilized metal affinity chromatography (IMAC) for initial capture

  • Ion exchange chromatography for intermediate purification

  • Size exclusion chromatography for final polishing and buffer exchange

• Quality control assessments:

  • SDS-PAGE with Coomassie staining for purity (aim for >95%)

  • Western blotting with anti-His antibodies to confirm identity

  • Mass spectrometry to verify protein integrity

  • Dynamic light scattering to assess aggregation state

  • Enzymatic activity assays using fluorescently labeled lipid II substrates

• Storage optimization:

  • Test stability in different buffer compositions (vary pH, salt concentration)

  • Determine optimal glycerol concentration (typically 10-20%)

  • Evaluate freeze-thaw stability versus flash-freezing aliquots

  • Monitor activity retention during storage at -80°C

For researchers working with S. dublin isolates with varying antimicrobial resistance profiles (as shown in the search results ), be aware that differences in membrane composition might affect extraction efficiency and protein stability, necessitating strain-specific optimization.

How can researchers differentiate between effects of mtgA inhibition and secondary effects of antimicrobial resistance mechanisms?

Differentiating between direct effects of mtgA inhibition and secondary effects of antimicrobial resistance mechanisms requires careful experimental design and appropriate controls. This distinction is particularly important when studying S. dublin strains that carry multiple resistance determinants, as documented in the search results . Researchers should implement the following methodological approach:

• Create isogenic strain pairs:

  • Generate knockout mutants of mtgA (ΔmtgA) in both antimicrobial-resistant and susceptible backgrounds

  • Create complemented strains where mtgA is expressed from a plasmid in the ΔmtgA background

  • Develop strains with catalytically inactive mtgA (point mutations in active site)

• Design a comprehensive phenotypic analysis matrix:

  • Compare growth curves in standard media and under cell wall stress conditions

  • Analyze peptidoglycan composition using HPLC and mass spectrometry

  • Measure susceptibility to various antimicrobials with different mechanisms of action

  • Assess cell morphology using electron microscopy

• Implement chemical-genetic approaches:

  • Use selective mtgA inhibitors (if available) across strain collection

  • Compare with effects of β-lactam antibiotics and other cell wall-targeting agents

  • Perform dose-response experiments with both classes of compounds

• Transcriptomic and proteomic analysis:

  • Compare gene expression profiles between isogenic strains under mtgA inhibition

  • Identify differentially regulated pathways that might represent compensatory mechanisms

  • Focus on cell envelope stress response genes

• In vitro enzymatic assays:

  • Purify mtgA from different strain backgrounds

  • Measure enzymatic activity with and without potential inhibitors

  • Assess whether resistance-associated modifications alter inhibitor binding

Based on the search results showing various resistance profiles in S. dublin isolates , researchers should be particularly attentive to strains carrying blaCMY-2, which confers resistance to cephalosporins and may affect cell wall synthesis pathways independently of mtgA function.

What statistical approaches are most appropriate for analyzing enzymatic activity data from recombinant Salmonella dublin mtgA variants?

When analyzing enzymatic activity data from recombinant S. dublin mtgA variants, researchers should implement robust statistical methodologies that account for the complex nature of enzymatic reactions and potential experimental variability. Based on established practices in enzymology and the nature of transglycosylase activity, the following statistical approaches are recommended:

• Enzyme kinetics analysis:

  • Fit Michaelis-Menten models to determine Km and Vmax parameters

  • Use non-linear regression rather than linearization methods (Lineweaver-Burk)

  • Calculate catalytic efficiency (kcat/Km) with propagated error

  • For variants showing substrate inhibition, apply appropriate modified models

• Comparative analysis of variants:

  • Use ANOVA with post-hoc tests (Tukey's HSD) for multiple variant comparisons

  • Apply Bonferroni or Benjamini-Hochberg corrections for multiple testing

  • Consider mixed-effects models if measurements are nested or repeated

• Dose-response relationships for inhibitors:

  • Fit four-parameter logistic models to determine IC50 values

  • Calculate inhibition constants (Ki) using appropriate competitive, non-competitive, or uncompetitive models

  • Use global fitting approaches for comparing inhibitor effects across variants

• Reproducibility and robustness assessment:

  • Calculate intra-assay and inter-assay coefficients of variation

  • Implement bootstrapping to generate confidence intervals

  • Use sensitivity analysis to identify influential data points

• Integration with structural data:

  • Correlate activity measurements with structural features using regression models

  • Apply principal component analysis to identify patterns across multiple parameters

  • Consider Bayesian approaches for incorporating prior knowledge about structure-function relationships

When comparing mtgA variants derived from S. dublin strains with different antimicrobial resistance profiles (as shown in the search results ), researchers should consider potential confounding factors and include appropriate covariates in statistical models.

How should researchers interpret contradictory results between biochemical assays and in vivo models when studying mtgA function?

Contradictions between biochemical assays and in vivo models studying mtgA function require systematic investigation and thoughtful interpretation. These discrepancies often reveal important biological complexities rather than experimental errors. Researchers should approach such contradictions methodically:

• Validate both experimental systems:

  • Confirm assay specificity with appropriate controls

  • Verify in vivo model phenotypes through complementation studies

  • Assess whether contradictions are qualitative or quantitative in nature

• Consider biological context differences:

  • Evaluate whether in vivo conditions (pH, ion concentrations, crowding) differ significantly from in vitro conditions

  • Assess potential interactions with other peptidoglycan synthesis enzymes present only in vivo

  • Investigate regulatory mechanisms that might operate in vivo but not in vitro

• Examine methodological differences:

  • Compare substrate compositions between assays

  • Assess the impact of purification methods on enzyme functionality

  • Consider time-scale differences between rapid biochemical assays and slower in vivo processes

• Integrate multiple approaches:

  • Perform in situ enzymatic assays in permeabilized cells

  • Develop cell-free expression systems that better mimic cellular environments

  • Use advanced microscopy to visualize enzyme localization and activity in living cells

• Resolve contradictions through hypothesis testing:

  • Formulate specific hypotheses explaining observed contradictions

  • Design experiments targeting these hypotheses

  • Implement intermediate complexity models bridging the gap between biochemical and in vivo systems

The search results indicate that S. dublin strains exhibit diverse antimicrobial resistance profiles , suggesting that cell wall physiology may vary significantly between strains. These differences could contribute to apparent contradictions between controlled biochemical assays and more complex in vivo models, particularly when studying enzymes involved in peptidoglycan synthesis.

What emerging technologies could enhance the study of Salmonella dublin mtgA and its role in antimicrobial resistance?

Several cutting-edge technologies show promise for advancing understanding of S. dublin mtgA function and its relationship to antimicrobial resistance. Researchers should consider incorporating these emerging approaches:

• CRISPR-Cas9 genome editing:

  • Create precise point mutations in mtgA without disrupting genomic context

  • Develop CRISPR interference (CRISPRi) systems for conditional mtgA repression

  • Implement base editors for studying specific amino acid substitutions

  • Design multiplexed CRISPR screens to identify genetic interactions with mtgA

• Cryo-electron microscopy:

  • Determine high-resolution structures of mtgA in membrane environments

  • Visualize mtgA-substrate and mtgA-inhibitor complexes

  • Examine conformational changes during catalytic cycles

  • Study interactions with other peptidoglycan synthesis enzymes

• Advanced fluorescence techniques:

  • Apply single-molecule FRET to monitor mtgA conformational dynamics

  • Use super-resolution microscopy to track mtgA localization in living cells

  • Develop fluorescent peptidoglycan probes for real-time transglycosylase activity monitoring

  • Implement FRAP (Fluorescence Recovery After Photobleaching) to measure enzyme mobility

• Synthetic biology approaches:

  • Create minimal synthetic peptidoglycan synthesis systems

  • Design orthogonal transglycosylases with modified substrate specificities

  • Develop genetic circuits for controlled expression of mtgA variants

  • Engineer reporter systems for transglycosylase activity in vivo

• Computational methods:

  • Apply molecular dynamics simulations to study mtgA membrane interactions

  • Use machine learning to predict functional consequences of mtgA mutations

  • Implement systems biology modeling of cell wall synthesis networks

  • Develop virtual screening pipelines for mtgA inhibitor discovery

Given the connections between antimicrobial resistance and mobile genetic elements highlighted in the search results , researchers should pay particular attention to how these emerging technologies can illuminate the relationship between mtgA function and the broader resistance landscape in S. dublin.

How might research on Salmonella dublin mtgA inform broader questions about peptidoglycan synthesis inhibitors as alternative antimicrobials?

Research on S. dublin mtgA has significant implications for developing novel peptidoglycan synthesis inhibitors as alternative antimicrobials, particularly in the context of increasing antimicrobial resistance documented in the search results . This research trajectory can inform broader questions through several methodological approaches:

• Comparative analysis across pathogens:

  • Identify conserved and divergent features of mtgA across Salmonella serotypes and other Enterobacteriaceae

  • Assess whether inhibitor sensitivity correlates with specific structural features

  • Determine the evolutionary constraints on mtgA sequence and structure

  • Evaluate the potential for narrow-spectrum versus broad-spectrum inhibitors

• Resistance mechanism investigations:

  • Characterize potential resistance mechanisms against mtgA inhibitors

  • Assess whether existing antimicrobial resistance determinants (like those in the MDR S. dublin isolates ) affect mtgA inhibitor efficacy

  • Determine the barrier to resistance for different inhibitor classes

  • Measure fitness costs associated with mtgA modifications conferring inhibitor resistance

• Combination therapy approaches:

  • Test synergistic effects between mtgA inhibitors and conventional antibiotics

  • Evaluate whether mtgA inhibitors can restore sensitivity to β-lactams in resistant strains

  • Develop dual-targeting molecules affecting multiple steps in peptidoglycan synthesis

  • Assess the impact of efflux pump inhibitors on mtgA inhibitor efficacy

• Translational research pathways:

  • Develop in vitro-in vivo correlation models for mtgA inhibitor activity

  • Implement pharmacokinetic/pharmacodynamic modeling for optimized dosing regimens

  • Assess potential off-target effects on host glycosyltransferases

  • Design delivery systems enhancing inhibitor access to the periplasmic space

• One Health considerations:

  • Evaluate mtgA inhibitor efficacy against S. dublin isolates from various hosts and environments

  • Assess impact on commensal microbiota composition and function

  • Determine potential for environmental persistence and ecological effects

  • Model the evolution of resistance under different usage scenarios

The search results indicate that S. dublin isolates frequently carry multiple antimicrobial resistance determinants , presenting a valuable opportunity to study how novel mtgA inhibitors might circumvent existing resistance mechanisms and provide alternative treatment options for multidrug-resistant infections.