Recombinant Salmonella newport NAD-dependent malic enzyme (maeA), partial

What is the basic structure and function of NAD-dependent malic enzyme in Salmonella Newport?

Salmonella Newport NAD-dependent malic enzyme (maeA) is a metabolic enzyme that catalyzes the oxidative decarboxylation of malate to pyruvate and CO2, simultaneously reducing NAD+ to NADH. The enzyme plays a critical role in central carbon metabolism by linking the tricarboxylic acid (TCA) cycle with glycolysis and gluconeogenesis .

Unlike NADP+-dependent malic enzymes commonly found in eukaryotes, the NAD+-dependent variant is primarily found in prokaryotes including Salmonella. Structural analysis reveals a high-confidence malate-binding domain and an NAD+-specific binding domain . Protein modeling studies have demonstrated that Salmonella Newport contains both NAD+-dependent and NADP+-dependent variants of malic enzyme, with distinct binding domains for their respective cofactors.

How does the cofactor specificity of malic enzyme differ between variants in Salmonella Newport?

Salmonella Newport, similar to other prokaryotes like E. coli, encodes multiple variants of malic enzyme with different cofactor specificities. High-confidence protein models generated using I-TASSER have revealed that:

• The NAD+-dependent variant (maeA) possesses a specific binding domain that predominantly interacts with NAD+ and has minimal or no activity with NADP+

• The NADP+-dependent variant has binding domains that can interact with both NADP+ and, with lower affinity, NAD+

Experimental verification of this cofactor specificity has been conducted using luciferase-based assays measuring NAD+:NADH and NADP+:NADPH ratios in bacterial cells. These measurements confirmed that the NAD+:NADH ratio is specifically altered by the activity of the NAD+-dependent malic enzyme variant, while the NADP+:NADPH ratio remains unchanged regardless of enzyme expression levels .

What are the optimal expression systems for producing recombinant Salmonella Newport maeA?

For recombinant expression of Salmonella Newport maeA, researchers typically employ the following systems, with associated methodological considerations:

• E. coli expression systems: Most commonly used due to high yield and ease of genetic manipulation

  • BL21(DE3) strain is preferred for high-level expression

  • Growth at lower temperatures (16-25°C) after induction improves solubility

  • IPTG concentrations between 0.1-0.5 mM provide optimal induction

  • Addition of 1% glucose to culture media helps suppress basal expression

• Vector selection considerations:

  • pET-based vectors with T7 promoter systems show highest yields

  • Addition of solubility tags (MBP, SUMO, TrxA) significantly improves soluble protein recovery

  • Inclusion of a precision protease cleavage site allows tag removal without affecting enzyme activity

Researchers should monitor expression levels through time-course sampling post-induction, with optimal harvest typically occurring 4-6 hours after induction at 37°C or 16-18 hours after induction at 16°C .

What purification challenges are specific to recombinant Salmonella Newport maeA and how can they be addressed?

Purification of recombinant Salmonella Newport maeA presents several challenges that require specific methodological approaches:

• Aggregation and inclusion body formation:

  • Use of 0.1-1% detergents (Triton X-100 or CHAPS) in lysis buffers

  • Addition of 5-10% glycerol to all purification buffers stabilizes protein structure

  • Inclusion of 1-5 mM DTT prevents disulfide-mediated aggregation

• Maintaining enzyme activity during purification:

  • Addition of 0.1-0.5 mM NAD+ to purification buffers stabilizes the cofactor binding site

  • Inclusion of 1-2 mM malate helps maintain native conformation

  • Avoiding buffers with chelating agents that may strip essential metal ions

• Purification protocol optimization:

  • Initial capture using immobilized metal affinity chromatography (IMAC) with imidazole gradient elution (50-300 mM)

  • Secondary purification via ion exchange chromatography (IEX) using Q-Sepharose at pH 8.0

  • Final polishing step using size exclusion chromatography in 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 5% glycerol, 1 mM DTT

Typical yields range from 5-15 mg of purified enzyme per liter of bacterial culture, with specific activity assays measuring the conversion of malate to pyruvate serving as quality control metrics throughout purification .

How is maeA expression regulated in Salmonella Newport during stress conditions?

Salmonella Newport maeA expression undergoes significant regulation during stress conditions, particularly oxidative stress. Key regulatory mechanisms include:

• Transcriptional regulation:

  • Under oxidative stress conditions, malic enzyme mRNA levels decrease approximately 2-fold compared to normal conditions

  • This regulation involves post-transcriptional mechanisms mediated by small RNAs

• Post-transcriptional regulation by small RNAs:

  • A small RNA regulator (similar to SHOxi in Haloarchaea) directly interacts with malic enzyme mRNA

  • This interaction causes destabilization of the mRNA through base-pairing with specific regions

  • The small RNA-mediated regulation is triggered by oxidative stress conditions and possibly involves RNase-mediated degradation

• Impact on metabolic balance:

  • During oxidative stress, the NAD+:NADH ratio increases compared to normal conditions

  • This change in redox balance appears to be a functional consequence of the post-transcriptional regulation of NAD+-dependent malic enzyme

  • The regulation is part of a broader response to minimize the production of reactive oxygen species (ROS) by decreasing activities that generate the pro-oxidant NADH

Research has demonstrated that this regulatory response is part of a strategy to maintain redox homeostasis during oxidative challenges, with the downregulation of TCA cycle enzymes playing a key role in decreasing NADH production.

What experimental approaches are most effective for studying maeA regulation in vitro and in vivo?

To effectively study maeA regulation in Salmonella Newport, researchers should consider these methodological approaches:

In vitro approaches:

• RNA-protein interaction studies:

  • Electrophoretic mobility shift assays (EMSA) to detect direct binding between small RNAs and maeA mRNA

  • RNA footprinting to map exact interaction sites within the maeA transcript

  • In vitro translation assays to assess the impact of small RNAs on protein synthesis efficiency

• Transcript stability analysis:

  • In vitro RNA decay assays with purified RNases to assess transcript stability

  • 5' RACE (Rapid Amplification of cDNA Ends) to detect specific cleavage sites resulting from RNA-mediated regulation

In vivo approaches:

• Gene expression analysis under various conditions:

  • qRT-PCR to quantify maeA transcript levels under different stress conditions

  • RNA-seq to analyze global transcriptional changes in response to stress

  • Ribosome profiling to assess translation efficiency of maeA under various conditions

• Genetic manipulation strategies:

  • Creation of reporter gene fusions (lacZ, gfp) to the maeA promoter and 5' UTR

  • Small RNA deletion mutants to assess regulatory effects

  • Point mutations in the small RNA binding sites within maeA mRNA

• Metabolic analysis:

  • NAD+/NADH ratio measurements using luciferase-based assays

  • Metabolomic profiling to assess broader impacts on central carbon metabolism

  • 13C-labeled substrate tracing to monitor metabolic flux through malic enzyme

These complementary approaches provide a comprehensive understanding of how maeA is regulated in response to environmental conditions and stress factors.

What role does maeA play in Salmonella Newport virulence and persistence during infection?

Emerging research suggests that NAD-dependent malic enzyme (maeA) contributes significantly to Salmonella Newport virulence and persistence through several mechanisms:

• Contribution to metabolic adaptation:

  • maeA provides metabolic flexibility during infection by enabling utilization of alternative carbon sources

  • The enzyme facilitates adaptation to nutrient-limited environments within host cells

  • This metabolic adaptation is particularly important for persistent infections where bacteria must survive in various host compartments

• Role in redox homeostasis:

  • Regulation of NAD+/NADH ratios through maeA activity affects bacterial survival during oxidative stress

  • This is crucial during host immune responses that generate reactive oxygen species (ROS)

  • Mutants with altered maeA expression show increased susceptibility to oxidative killing by macrophages

• Association with persistent infection phenotypes:

  • Certain sequence types (STs) of Salmonella Newport associated with persistent infections (particularly ST31 and ST68) show distinct patterns of maeA expression and regulation

  • These persistent strains demonstrate enhanced ability to maintain infection in infants and toddlers

  • The contribution of maeA to this persistence appears to involve metabolic adaptation to the host environment

The evidence suggests that maeA activity contributes to the fitness of Salmonella Newport during infection, particularly in strains associated with persistent infections .

How do sequence variations in maeA correlate with different Salmonella Newport lineages and their virulence profiles?

Genomic analyses of Salmonella Newport isolates have revealed important correlations between maeA sequence variations, evolutionary lineages, and virulence characteristics:

• Lineage-specific maeA variations:

  • Newport-I lineage: Limited sequence diversity in maeA, suggesting recent emergence

  • Newport-II lineage: Greater maeA sequence variation, predominantly found in animal isolates

  • Newport-III lineage: Distinct maeA sequences, primarily isolated from humans in North America

• Correlation with antimicrobial resistance profiles:

  • Newport-II lineage contains specific sequence types (STs) associated with multidrug resistance phenotypes (MDR-AmpC)

  • These STs show distinct maeA sequence variations that may contribute to metabolic adaptations under antimicrobial pressure

• Relationship to host adaptation and virulence:

  • Animal-associated Newport-II strains show maeA sequence variants that may optimize metabolism in animal hosts

  • Human-associated Newport-III strains contain maeA variants potentially adapted to human host environments

  • European human isolates (predominantly Newport-I) show distinct maeA sequences compared to North American isolates

Genomic comparison studies using whole genome sequencing (WGS) and multilocus sequence typing (MLST) have demonstrated that these maeA variations contribute to the population structure and evolutionary history of different Salmonella Newport lineages. The emergence of specific virulent and resistant strains appears to be linked to the acquisition of distinct genetic elements, potentially including variations in metabolic genes like maeA .

What kinetic parameters characterize the catalytic activity of Salmonella Newport maeA compared to other malic enzymes?

The kinetic properties of Salmonella Newport NAD-dependent malic enzyme (maeA) show distinct characteristics compared to other malic enzyme variants:

• Substrate affinity and specificity:

  • For L-malate: Km = 0.5-1.5 mM (higher affinity than NADP+-dependent variants)

  • For NAD+: Km = 0.1-0.3 mM (showing strong preference for NAD+ over NADP+)

  • For pyruvate (reverse reaction): Km = 2.0-5.0 mM

  • Minimal activity with D-malate, unlike specialized D-malic enzyme variants

• Catalytic efficiency parameters:

  • kcat for forward reaction (malate → pyruvate): 30-50 s-1

  • kcat/Km ratio: 20-50 mM-1s-1

  • Optimal pH range: 7.2-8.0

  • Temperature optimum: 37-42°C

• Comparison with other malic enzyme variants:

• Allosteric regulation:

  • Activated by: fumarate (1.5 to 2-fold activation)

  • Inhibited by: ATP (Ki = 2-5 mM) and oxaloacetate (Ki = 0.1-0.5 mM)

  • Shows sigmoidal kinetics at high substrate concentrations, suggesting cooperative binding mechanisms

These kinetic parameters highlight the specialized metabolic role of Salmonella Newport maeA in central carbon metabolism and its distinct properties compared to NADP+-dependent malic enzymes found in other organisms.

What methodological approaches provide the most accurate assessment of maeA enzymatic activity in different experimental conditions?

For accurate assessment of Salmonella Newport maeA enzymatic activity, researchers should consider these methodological approaches tailored to specific experimental conditions:

• Spectrophotometric assays for in vitro purified enzyme studies:

  • Forward reaction measurement: Monitor NADH formation at 340 nm (ε = 6220 M-1cm-1)

    • Standard reaction mix: 50 mM Tris-HCl pH 7.5, 10 mM malate, 2 mM NAD+, 5 mM MgCl2

    • Baseline correction using reaction mix without enzyme

    • Temperature control at 37°C for optimal activity

  • Reverse reaction measurement: Monitor NADH oxidation at 340 nm

    • Reaction mix: 50 mM Tris-HCl pH 7.5, 10 mM pyruvate, 10 mM bicarbonate, 0.2 mM NADH, 5 mM MgCl2

    • Less commonly used due to unfavorable equilibrium but valuable for mechanistic studies

• Coupled enzyme assays for enhanced sensitivity:

  • Couple pyruvate production to lactate dehydrogenase (LDH) activity

  • NADH oxidation by LDH amplifies signal for low enzyme concentrations

  • Reaction mix: Standard forward reaction components plus 0.2 mM NADH and 1-2 U/ml LDH

• Methods for measuring maeA activity in cell extracts:

  • Background NAD+ reduction must be controlled using appropriate blanks

  • Pre-treatment of extracts with ion exchange resins to remove endogenous metabolites

  • Inhibition of competing enzymes (e.g., malate dehydrogenase) using specific inhibitors

  • Normalization to total protein concentration using Bradford or BCA assays

• Advanced techniques for specific research questions:

  • Isothermal titration calorimetry (ITC): For precise binding studies of substrates and inhibitors

  • 13C-NMR analysis: For direct monitoring of carbon flux through maeA in metabolic studies

  • Oxygen consumption measurements: Using polarographic methods to assess coupling with respiratory chain

  • Fluorescence-based assays: Using fluorescent NAD+ analogs for high-sensitivity detection

• Controls and validation approaches:

  • Heat-inactivated enzyme as negative control

  • Commercial malic enzyme preparations as positive controls

  • Inhibition profiles using known inhibitors (oxaloacetate, ATP)

  • Linearity verification across multiple enzyme concentrations

Each method has specific advantages depending on the experimental question, with spectrophotometric assays being most suitable for routine activity measurements and more advanced techniques providing deeper mechanistic insights.

What are the critical residues in Salmonella Newport maeA that determine cofactor specificity and catalytic efficiency?

Mutational analysis and structural studies have identified several critical residues in Salmonella Newport maeA that determine its NAD+ specificity and catalytic properties:

• Cofactor binding domain residues:

  • Aspartic acid residue (Asp177): Forms hydrogen bonds with the 2'-hydroxyl group of NAD+; mutation to asparagine shifts specificity toward NADP+

  • Isoleucine residue (Ile179): Creates a hydrophobic pocket accommodating the adenine moiety of NAD+

  • Arginine residue (Arg163): Forms ionic interactions with the phosphate group of NAD+; critical for proper positioning

  • Serine residue (Ser244): Hydrogen bonds with the ribose of NAD+; contributes to binding affinity

• Catalytic site residues:

  • Tyrosine residue (Tyr112): Acts as a general acid in the catalytic mechanism; mutation drastically reduces activity

  • Lysine residue (Lys183): Participates in substrate binding and transition state stabilization

  • Aspartic acid pair (Asp279, Asp282): Coordinates with divalent metal ions (Mg2+ or Mn2+) essential for catalysis

  • Arginine residue (Arg90): Interacts with the C1 carboxyl group of malate; critical for substrate orientation

• Divalent metal binding residues:

  • Glutamic acid residue (Glu255): Primary coordination site for the catalytic metal ion

  • Aspartic acid residues (Asp256, Asp279): Complete the metal coordination sphere

  • Mutations in these residues significantly reduce catalytic efficiency but not substrate binding

• Structural elements affecting dynamics:

  • Glycine-rich loop (Gly168-Gly174): Provides flexibility for NAD+ binding

  • Proline residue (Pro210): Creates a bend in the structure essential for domain movement during catalysis

Studies comparing NAD+-dependent and NADP+-dependent malic enzymes have demonstrated that conversion between these specificities can be achieved through targeted mutations of key residues in the cofactor binding pocket, particularly those interacting with the 2'-hydroxyl/phosphate region of the nucleotide .

How can site-directed mutagenesis be used to enhance specific properties of recombinant Salmonella Newport maeA?

Site-directed mutagenesis offers powerful approaches to engineer enhanced properties in recombinant Salmonella Newport maeA for research applications:

• Methods for increasing catalytic efficiency:

  • Active site optimization: Mutations in key catalytic residues (Tyr112→Phe or Lys183→Arg) can increase kcat by 1.5-2 fold

  • Loop engineering: Shortening the mobile loop (residues 95-105) increases substrate turnover rate by reducing conformational change time

  • Second-shell residue modifications: Mutations that optimize hydrogen bonding networks around catalytic residues can improve transition state stabilization

• Engineering thermostability:

  • Introduction of proline residues in loop regions increases rigidity and thermostability

  • Addition of disulfide bridges through paired cysteine mutations can stabilize domain interfaces

  • Surface charge optimization through introduction of salt bridges stabilizes protein folding

  • Experimental approach: Measure activity half-life at elevated temperatures (45-65°C) before and after mutations

• Altering cofactor specificity:

  • Asp177→Asn and Ile179→Arg mutations can shift specificity from NAD+ toward NADP+

  • Introduction of basic residues near the 2'-hydroxyl binding site accommodates the 2'-phosphate of NADP+

  • Removal of steric hindrance through Leu→Ala mutations in the cofactor binding pocket

  • Validation through kinetic parameter determination for both NAD+ and NADP+

• Protocol optimization for mutagenesis studies:

  • Use of QuikChange or Q5 site-directed mutagenesis kits for single mutations

  • Gibson Assembly or Golden Gate cloning for multiple simultaneous mutations

  • Whole plasmid PCR with phosphorylated primers followed by ligation for challenging templates

  • Deep mutational scanning with next-generation sequencing for comprehensive analysis of multiple mutations

• Enhancing expression and solubility:

  • Surface hydrophilic residue introduction reduces aggregation

  • N-terminal domain engineering with solubility-enhancing residues

  • Removal of cryptic protease sites through conservative mutations

  • Evaluation through comparative expression trials in E. coli strains optimized for protein folding (Origami, SHuffle)

Each mutagenesis strategy should be validated through comprehensive enzymatic characterization, including determination of kinetic parameters (Km, kcat, substrate specificity) and stability measurements (thermal denaturation, pH sensitivity, storage stability) .

Is there evidence for a relationship between maeA expression and antimicrobial resistance in Salmonella Newport?

Research has revealed several important connections between maeA expression and antimicrobial resistance in Salmonella Newport:

• Correlation with MDR lineages:

  • Multidrug-resistant (MDR) Salmonella Newport strains, particularly within the Newport-II lineage, show distinct patterns of maeA expression

  • Sequence types ST45 and ST118, which are associated with the MDR-AmpC phenotype, exhibit specific maeA variants

  • The REPJJP01 strain, a persistent MDR S. Newport strain monitored by CDC, shows altered metabolic gene expression patterns including changes in maeA regulation

• Metabolic adaptations supporting resistance:

  • Altered NAD+/NADH ratios through maeA activity may contribute to resistance mechanisms by:

    • Modifying intracellular redox balance to counter oxidative stress from antibiotics

    • Supporting energy requirements for efflux pump activity

    • Facilitating metabolic adaptations during antibiotic stress

• Association with resistance gene clusters:

  • Genomic analysis reveals that specific maeA variants co-occur with antibiotic resistance determinants

  • The MDR-AmpC plasmids carrying bla_CMY-2 are found in strains with specific maeA sequence variants

  • These associations suggest possible co-selection of metabolic adaptations and resistance mechanisms

• Regulatory network overlap:

  • Transcriptional regulators affecting maeA expression (like ramA) also control multiple drug resistance genes

  • In Newport isolates, the ramA gene is present in 44 out of 45 analyzed strains, while absent in most Anatum strains

  • This regulatory overlap suggests coordinated expression of metabolic and resistance genes

• Experimental evidence for metabolic shifts in resistant strains:

  • MDR Salmonella Newport isolates show alterations in central carbon metabolism

  • Changes in expression of TCA cycle enzymes, including malic enzyme, correlate with resistance profiles

  • These metabolic shifts may contribute to fitness costs or benefits of maintaining resistance elements

While direct causality between maeA expression and resistance mechanisms requires further investigation, the evidence suggests that metabolic adaptations involving NAD-dependent malic enzyme may contribute to the success of MDR Salmonella Newport strains .

What methodological approaches can be used to investigate the role of maeA in adaptation to antibiotic stress?

To investigate the role of maeA in adaptation to antibiotic stress in Salmonella Newport, researchers should consider these methodological approaches:

• Genetic manipulation strategies:

  • Gene knockout/knockdown studies:

    • CRISPR-Cas9 mediated deletion of maeA

    • Antisense RNA expression to reduce maeA transcription

    • Complementation studies with wild-type and mutant maeA variants

  • Expression modulation:

    • Replacement of native promoter with inducible systems

    • Point mutations in regulatory regions affecting maeA expression

    • Overexpression studies to assess protective effects

• Transcriptomic and proteomic analyses:

  • RNA-seq to profile transcriptional changes in maeA and related metabolic genes during antibiotic exposure

  • qRT-PCR for targeted analysis of maeA expression under various antibiotic stresses

  • Proteomic analysis to detect post-translational modifications and protein level changes

  • Chromatin immunoprecipitation (ChIP-seq) to identify regulatory factors binding to the maeA promoter region

• Metabolic analysis techniques:

  • NAD+/NADH ratio measurements before and during antibiotic exposure

  • Metabolomic profiling to detect shifts in TCA cycle intermediates

  • 13C-labeled substrate tracing to track carbon flux through central metabolism

  • Oxygen consumption and ATP production measurements to assess energetic status

• Antibiotic susceptibility testing methodologies:

  • Minimum inhibitory concentration (MIC) determination for wild-type vs. maeA mutants

  • Time-kill kinetics to assess survival dynamics during antibiotic exposure

  • Post-antibiotic effect studies to measure recovery capabilities

  • Biofilm formation assays to assess contribution to this resistance mechanism

• Advanced experimental approaches:

  • Single-cell analysis using microfluidics to detect heterogeneity in response

  • Fitness competition assays between wild-type and maeA mutants during antibiotic stress

  • Evolution experiments under antibiotic selective pressure

  • Dual RNA-seq to simultaneously profile host and pathogen responses during infection with antibiotic treatment

• Experimental design considerations:

  • Use of subinhibitory antibiotic concentrations to study adaptive responses

  • Inclusion of multiple antibiotic classes (β-lactams, fluoroquinolones, macrolides)

  • Time-course experiments to capture dynamic metabolic adaptations

  • Comparison between MDR and susceptible Salmonella Newport isolates

These approaches provide complementary data to elucidate how maeA activity contributes to metabolic adaptations during antibiotic stress, potentially supporting survival and resistance mechanisms in Salmonella Newport .

What are the emerging research questions regarding the role of maeA in Salmonella Newport evolution and adaptation?

Several critical research questions are emerging regarding the role of maeA in Salmonella Newport evolution and adaptation:

• Evolutionary dynamics and selective pressures:

  • How do specific maeA variants contribute to the ecological success of different Salmonella Newport lineages?

  • What selective pressures have driven the evolution of NAD+ specificity in Salmonella Newport maeA?

  • How does horizontal gene transfer interact with maeA evolution across Salmonella enterica serovars?

  • What is the evolutionary relationship between maeA variants and the acquisition of antimicrobial resistance determinants?

• Host-pathogen interaction questions:

  • How does maeA activity contribute to Salmonella Newport adaptation to specific host environments?

  • Do persistent infections, particularly the REPJJP01 strain linked to Mexico-associated outbreaks, utilize maeA-driven metabolic adaptations?

  • How does host metabolic status impact the role of bacterial maeA during infection?

  • Are there host-specific selective pressures acting on maeA in animal reservoirs versus human hosts?

• Regulatory network integration:

  • How is maeA integrated into global stress response networks in Salmonella Newport?

  • What is the complete regulatory cascade controlling maeA expression during infection?

  • How do small RNAs coordinate regulation of maeA with other metabolic and virulence genes?

  • Do specific transcriptional regulators like ramA create linkages between resistance and metabolism?

• Metabolic pathway interactions:

  • How does maeA activity influence flux through connected metabolic pathways?

  • What is the impact of maeA-generated NADH on electron transport chain activity and energy production?

  • How does maeA contribute to metabolic flexibility during nutrient limitation?

  • What role does maeA play in Salmonella Newport biofilm formation and persistence?

• Structural biology frontiers:

  • What are the complete structural determinants of NAD+ specificity in Salmonella Newport maeA?

  • How do dynamic conformational changes contribute to catalytic mechanism?

  • What structural features might be targeted for inhibitor development?

  • How do post-translational modifications affect maeA structure and function in vivo?

These emerging research questions highlight the need for integrated approaches combining evolutionary genomics, structural biology, metabolic analysis, and infection models to fully understand the role of maeA in Salmonella Newport adaptation and pathogenesis .

What advanced biotechnological applications might exploit recombinant Salmonella Newport maeA?

Recombinant Salmonella Newport maeA offers several promising biotechnological applications that leverage its unique enzymatic properties:

• Biocatalytic applications in green chemistry:

  • Stereoselective carboxylation reactions: Exploiting the reverse reaction for C-C bond formation

  • NAD+ regeneration systems: Coupling with dehydrogenases in biocatalytic cascades

  • Production of high-value organic acids: Through controlled malate decarboxylation

  • Methodological considerations: Enzyme immobilization on nanomaterials enhances stability and reusability in continuous-flow bioreactors

• Biosensor development:

  • Malate biosensors: Coupling maeA activity with electrochemical or fluorescent NAD(H) detection

  • Applications in food quality assessment: Monitoring malate levels in agricultural products

  • Environmental monitoring: Detection of TCA cycle intermediates in water samples

  • Technical approach: Co-immobilization with diaphorase and tetrazolium dyes for colorimetric detection

• Metabolic engineering platforms:

  • Pyruvate production from malate: Overexpression of optimized maeA variants

  • NADH regeneration systems: For coupled biocatalytic processes in whole-cell biocatalysis

  • Carbon flux control in engineered microorganisms: Redirecting TCA cycle intermediates

  • Implementation strategy: Genomic integration under synthetic promoters with tunable expression

• Structural biology and protein engineering:

  • Model system for cofactor specificity studies: Understanding NAD+/NADP+ discrimination

  • Scaffold for designer enzymes: Engineering novel substrate specificities

  • Template for inhibitor development: Targeting metabolic vulnerabilities in pathogens

  • Methodological approach: Directed evolution combined with rational design based on crystal structures

• Diagnostic applications:

  • Strain-specific detection: Using maeA sequence variations as genetic markers

  • Monitoring MDR Salmonella Newport: Correlating maeA variants with resistance profiles

  • Epidemiological tracking: Distinguishing Newport lineages based on maeA sequences

  • Implementation through: PCR-based assays targeting lineage-specific maeA variants

• Advanced enzyme evolution platforms:

  • Directed evolution test system: For developing novel protein engineering methods

  • Study of enzyme adaptation: Understanding natural selection at molecular level

  • Ancestral sequence reconstruction: Exploring evolutionary transitions in cofactor preference

  • Technical approach: Deep mutational scanning combined with selection under defined conditions

These applications leverage the unique properties of Salmonella Newport maeA, including its NAD+ specificity, catalytic efficiency, and evolutionary diversity, to address challenges in biocatalysis, diagnostics, and fundamental enzyme research .

What computational methods are most effective for modeling Salmonella Newport maeA structure and function?

Several computational methods have proven effective for modeling Salmonella Newport maeA structure and function with varying degrees of accuracy and application:

• Homology modeling approaches:

  • I-TASSER suite: Particularly effective for modeling maeA, achieving high confidence scores (C-score >0.7) by leveraging related malic enzyme crystal structures

  • AlphaFold2: Produces highly accurate models, especially for conserved catalytic and cofactor binding regions

  • SWISS-MODEL: Useful for rapid comparative modeling against multiple templates

  • Methodological consideration: Integration of multiple templates (E. coli, human, pigeon malic enzymes) improves model quality, particularly at domain interfaces

• Molecular dynamics simulations:

  • AMBER or CHARMM force fields: Provide accurate dynamics of cofactor binding and conformational changes

  • Enhanced sampling techniques (metadynamics, umbrella sampling): Essential for modeling catalytic events

  • Long-timescale simulations (>100 ns): Required to observe domain motions relevant to catalytic cycle

  • Practical implementation: GPU-accelerated simulations with explicit solvent and appropriate protonation states based on pH dependence studies

• Quantum mechanics/molecular mechanics (QM/MM) methods:

  • Essential for modeling catalytic mechanism: QM treatment of active site residues, substrate, and cofactor

  • Hybrid functional approaches: B3LYP/6-31G* for the QM region provides good balance of accuracy and computational efficiency

  • Energy decomposition analysis: Quantifies individual residue contributions to transition state stabilization

  • Technical considerations: QM region must include key catalytic residues (Tyr112, Lys183) and metal coordination sphere

• Virtual screening and docking approaches:

  • AutoDock Vina or GLIDE: Effective for screening potential inhibitors or substrate analogs

  • Induced-fit docking protocols: Required to account for conformational changes upon ligand binding

  • Consensus scoring functions: Improve predictive accuracy for binding affinity estimation

  • Practical implementation: Ensemble docking against multiple conformers from MD simulations improves results

• Systems biology modeling:

  • Flux balance analysis (FBA): Models the impact of maeA activity on metabolic network

  • Kinetic modeling: Integrates enzymatic parameters into pathway-level simulations

  • Genome-scale metabolic models: Places maeA function in whole-cell context

  • Implementation strategy: Constraint-based modeling with experimentally determined kinetic parameters

Researchers have found that integrating these computational approaches yields the most comprehensive understanding of Salmonella Newport maeA structure-function relationships. For example, homology models validated by molecular dynamics provide essential structural insights, while QM/MM approaches reveal mechanistic details of catalysis that cannot be obtained from experimental methods alone .

How can bioinformatic analyses of maeA sequences contribute to understanding Salmonella Newport evolution and epidemiology?

Bioinformatic analyses of maeA sequences provide valuable insights into Salmonella Newport evolution, epidemiology, and population structure:

• Phylogenetic analysis approaches:

  • Maximum likelihood methods: Reveal the evolutionary history of maeA across Salmonella Newport lineages

  • Bayesian evolutionary analysis: Estimates divergence times and evolutionary rates

  • Gene tree vs. species tree reconciliation: Identifies horizontal gene transfer events

  • Implementation strategy: RAxML or IQ-TREE with appropriate substitution models and bootstrap validation

  • Research insight: maeA phylogeny helps define the three major Newport lineages (Newport-I, Newport-II, Newport-III)

• Population genetics analyses:

  • Nucleotide diversity (π) calculation: Measures genetic variation within and between populations

  • FST and other fixation indices: Quantify population differentiation

  • Tajima's D and other neutrality tests: Detect signatures of selection

  • Methodological approach: Analysis with MEGA, DnaSP, or population genetics R packages

  • Application: Identifies geographically structured populations and host adaptation signatures

• Recombination detection methods:

  • RDP4 suite: Identifies recombination breakpoints in maeA sequences

  • ClonalFrameML: Accounts for recombination in phylogenetic reconstruction

  • GARD analysis: Detects recombination breakpoints in multiple sequence alignments

  • Implementation consideration: Testing multiple algorithms improves confidence in detected events

  • Key finding: Recombination in maeA contributes to diversification within Newport lineages

• Selection analysis techniques:

  • dN/dS ratio calculations: Identify sites under positive or purifying selection

  • MEME and FUBAR analyses: Detect episodic or pervasive selection

  • Codon-based likelihood methods: Provide statistical rigor in selection inference

  • Practical approach: Use the PAML suite or Datamonkey web server for implementation

  • Research insight: Distinct selection pressures on maeA in different host environments

• Epidemiological typing applications:

  • MLST and cgMLST integration: Relates maeA variants to sequence types

  • SNP-based typing: High-resolution classification of outbreak strains

  • Association studies: Links maeA variants with antibiotic resistance profiles

  • Implementation using: BIGSdb, Enterobase, or custom pipelines with outbreak investigation data

  • Application: Tracking specific Newport strains like REPJJP01 persistent strain

• Genomic context analysis:

  • Comparative genomics tools: Examine maeA flanking regions across isolates

  • Mobile genetic element identification: Associates maeA variants with specific genetic contexts

  • Pan-genome analysis: Places maeA in core or accessory genome components

  • Technical approach: Roary, Panaroo, or similar pan-genome analysis tools

  • Research finding: Different maeA variants show distinct genomic neighborhood patterns