Recombinant Sinorhizobium medicae Glucose-6-phosphate isomerase (pgi), partial

What is Glucose-6-phosphate isomerase and what role does it play in Sinorhizobium medicae metabolism?

Glucose-6-phosphate isomerase (PGI), also known as phosphoglucose isomerase (EC 5.3.1.9), catalyzes the reversible isomerization of glucose-6-phosphate (G-6-P) to fructose-6-phosphate (F-6-P). This enzyme plays a central role in the sugar metabolism of various organisms, including bacteria in the Sinorhizobium genus . In S. medicae, PGI is particularly important for carbohydrate metabolism, serving as a key enzyme in the Embden-Meyerhof (EM) pathway that enables the bacterium to convert glucose into energy and metabolic intermediates necessary for growth and symbiotic functions.

To study PGI activity in laboratory settings, researchers typically employ spectrophotometric assays that measure either the formation of F-6-P from G-6-P or the reverse reaction. These assays can be coupled with auxiliary enzymes such as glucose-6-phosphate dehydrogenase to monitor reaction progression through changes in NADH or NADPH absorbance at 340 nm.

How does the structure of Sinorhizobium medicae PGI differ from other bacterial PGIs?

While specific structural data for S. medicae PGI is limited in the available literature, comparative analysis with other bacterial PGIs reveals important distinctions. Archaeal PGIs represent a novel type that differs significantly from the conserved PGI superfamily found in eubacteria and eucarya . For example, the PGI from Pyrococcus furiosus shows no significant sequence similarity to the conserved PGI superfamily found in most bacteria and eukaryotes .

To investigate these structural differences, researchers can employ:

• Multiple sequence alignment to identify conserved and variable residues

• Homology modeling to predict three-dimensional structure

• X-ray crystallography for precise structural determination

• Site-directed mutagenesis to identify catalytically important residues

These analyses can reveal specific adaptations in S. medicae PGI that might be linked to its symbiotic lifestyle or other aspects of its particular physiology within the context of plant-microbe interactions.

What are the optimal methods for expressing recombinant S. medicae PGI in E. coli?

Based on established protocols for recombinant enzyme expression, the following methodology is recommended for expressing S. medicae PGI in E. coli:

• Gene cloning strategy:

  • Amplify the pgi gene from S. medicae genomic DNA using PCR with high-fidelity polymerase

  • Clone the amplified gene into an expression vector (pET, pBAD, or pTrc) with an appropriate affinity tag

  • Verify the construct by sequencing to ensure correct orientation and absence of mutations

• Expression system selection:

  • Transform the construct into E. coli expression strains (BL21(DE3), Rosetta, or Arctic Express)

  • For enhanced expression, consider using E. coli Bactosome systems, which exhibit greater activity levels and turnover numbers compared to insect cell expression systems

  • Classic Bactosomes show excellent linearity over time, allowing longer incubations and generating better results

• Expression optimization:

  • Test various induction temperatures (16-37°C)

  • Optimize inducer concentrations (IPTG for pET systems)

  • Determine optimal induction duration (4-24 hours)

  • Consider codon optimization if initial expression levels are low

• Analytical validation:

  • Verify expression by SDS-PAGE and Western blotting

  • Assess solubility by analyzing soluble and insoluble fractions

  • Perform activity assays to confirm functional expression

The Bactosome expression system mentioned in source may be particularly suitable for S. medicae PGI expression, as these systems often provide superior activity and stability compared to conventional expression systems.

What challenges might arise during purification of recombinant S. medicae PGI and how can they be addressed?

Purification of recombinant S. medicae PGI presents several potential challenges that researchers should anticipate and address:

• Solubility issues and inclusion body formation:

  • Solution: Lower expression temperature (16-20°C), reduce inducer concentration, co-express with molecular chaperones, or use solubility-enhancing tags

  • Verification method: SDS-PAGE analysis of soluble and insoluble fractions after cell lysis

• Low expression yield:

  • Solution: Optimize codon usage for E. coli, use strains enriched with rare tRNAs, test different promoters

  • Quantification method: Western blot and densitometry to measure expression levels

• Loss of enzymatic activity:

  • Solution: Include stabilizers in buffers (glycerol, DTT), avoid freeze/thaw cycles, optimize pH and ionic strength

  • Monitoring method: Measure enzymatic activity at each purification step

• Contamination with E. coli proteins:

  • Solution: Use a combination of purification techniques (affinity chromatography, ion exchange, gel filtration)

  • Assessment method: SDS-PAGE, mass spectrometry

A typical purification protocol would include:

• Cell lysis (sonication or French press)

• Lysate clarification (high-speed centrifugation)

• Affinity chromatography (IMAC for His-tagged proteins)

• Tag cleavage (if necessary)

• Ion exchange chromatography

• Gel filtration for final polishing

By monitoring enzyme activity and purity at each step, researchers can optimize the protocol to maximize yield while preserving functionality.

How should researchers determine the kinetic parameters of recombinant S. medicae PGI?

A comprehensive kinetic characterization of recombinant S. medicae PGI should include the following methodological approach:

• Enzymatic assay optimization:

  • Determine optimal buffer conditions (typically Tris-HCl or phosphate, pH 7.0-8.0)

  • Establish temperature optima (generally 25-37°C)

  • Identify necessary cofactors (metal ions such as Mg²⁺ if required)

• Bidirectional activity measurement:

  • F-6-P → G-6-P direction: Couple with glucose-6-phosphate dehydrogenase (G6PDH) and monitor NADPH production at 340 nm

  • G-6-P → F-6-P direction: Couple with phosphofructokinase and aldolase, then measure resulting products

• Kinetic parameter determination:

  • Prepare a series of substrate concentrations (typically 0.1-10 times the estimated Km value)

  • Measure initial velocity for each concentration

  • Plot data using Lineweaver-Burk, Eadie-Hofstee, or Hanes-Woolf transformations

  • Calculate Km, Vmax, kcat, and kcat/Km (catalytic efficiency)

• Environmental factor profiling:

  • pH profile: Measure activity at different pH values (typically 5.0-9.0)

  • Temperature profile: Measure activity at different temperatures (typically 15-65°C)

  • Thermal stability: Incubate enzyme at various temperatures for different durations

• Inhibitor and activator effects:

  • Test potential inhibitory compounds (such as 6-phosphogluconate)

  • Determine inhibition type (competitive, non-competitive, uncompetitive)

  • Calculate inhibition constants (Ki)

This methodological framework provides a foundation for understanding how S. medicae PGI functions and how it may be adapted to its specific environmental niche and metabolic role within this symbiotic bacterium.

How does S. medicae PGI activity compare with those from other rhizobial species under symbiotic conditions?

Comparing PGI activity across rhizobial species under symbiotic conditions requires a systematic approach that addresses both enzymatic properties and physiological context:

• Comparative enzymatic analysis:

  • Isolate PGI from different rhizobial species (S. medicae, S. meliloti, Rhizobium spp., Bradyrhizobium spp.)

  • Measure kinetic parameters (Km, Vmax, kcat) under identical conditions

  • Compare substrate specificity and inhibition profiles

  • Assess enzyme stability under conditions mimicking the nodule environment

• Gene expression under symbiotic conditions:

  • Extract RNA from bacteria in different physiological states (free-living vs. bacteroids)

  • Perform RT-qPCR or RNA-seq to quantify pgi expression levels

  • Compare expression patterns across species and conditions

  • Correlate expression with symbiotic efficiency

• Mutant phenotype characterization:

  • Generate pgi mutants in different rhizobial species

  • Assess growth rates in free-living conditions

  • Evaluate nodulation efficiency and nitrogen fixation capacity

  • Perform metabolomic analysis to identify metabolic bottlenecks

Note: Table data is representative and would need to be determined experimentally.

This comparative approach would reveal how PGI activity has evolved across rhizobial species to optimize function in different symbiotic contexts and host environments.

How does S. medicae PGI contribute to establishing and maintaining symbiosis with Medicago plants?

The glucose-6-phosphate isomerase (PGI) in Sinorhizobium medicae plays several critical roles in establishing and maintaining effective symbiosis with Medicago host plants. These roles can be investigated through the following methodological approaches:

• Carbon metabolism during bacteroid differentiation:

  • PGI facilitates the glycolytic flux needed during the transformation of bacteria into bacteroids within root nodules

  • Methodology: Analyze gene expression using RT-qPCR or RNA-seq during different stages of nodulation; perform carbon flux analysis using 13C-labeled substrates

• Polysaccharide biosynthesis for symbiotic signaling:

  • PGI provides precursors for the synthesis of exopolysaccharides (EPS) and lipopolysaccharides (LPS)

  • S. medicae populations show high polymorphism in genes involved in polysaccharide synthesis, suggesting their importance in symbiotic adaptation

  • Methodology: Quantify polysaccharide production in wild-type vs. pgi mutants; analyze the structural composition of these polysaccharides using NMR and mass spectrometry

• Adaptation to microaerobic nodule conditions:

  • The enzyme must function efficiently in the low-oxygen environment of nodules

  • Methodology: Measure PGI activity under different oxygen tensions; compare in vitro enzyme properties with in vivo metabolic flux measurements

• Integration with symbiosis-specific metabolism:

  • Analysis of the S. medicae population reveals areas of low genetic divergence around important symbiotic genes

  • Methodology: Use metabolomic approaches to map metabolite levels in wild-type and pgi mutant bacteroids; perform isotope labeling studies to track carbon allocation

• Stress response during symbiotic establishment:

  • PGI contributes to bacterial survival during plant defense responses

  • Methodology: Challenge pgi mutants with oxidative stressors; measure survival rates and metabolic adaptations

Research on S. meliloti, a close relative of S. medicae, has demonstrated that genomic adaptations play crucial roles in symbiotic effectiveness . Similar approaches could be applied to understand the specific contribution of PGI in S. medicae symbiosis.

How do genomic variations in the pgi gene across S. medicae strains correlate with symbiotic performance?

Genomic variations in the pgi gene across different Sinorhizobium medicae strains can significantly impact symbiotic performance with host plants. A comprehensive investigation of this relationship requires the following methodological approach:

• Genomic sequence analysis:

  • Sequence the pgi gene from diverse S. medicae strains isolated from different geographical locations and hosts

  • Identify single nucleotide polymorphisms (SNPs), insertions/deletions, and other genetic variations

  • Construct phylogenetic trees to visualize relationships between variants

  • Methodology: Use next-generation sequencing followed by comparative genomic analysis

• Structure-function correlation:

  • Model the impact of amino acid substitutions on enzyme structure and function

  • Express and purify variant PGI proteins for biochemical characterization

  • Determine if variations alter substrate affinity, catalytic efficiency, or regulation

  • Methodology: Use protein modeling, site-directed mutagenesis, and enzyme kinetics

• Symbiotic phenotype assessment:

  • Evaluate nodulation efficiency, nitrogen fixation capacity, and competitive ability of different strains

  • Correlate phenotypic differences with specific pgi variants

  • Methodology: Plant inoculation experiments, acetylene reduction assays, competitive nodulation studies

• Genomic context analysis:

  • Determine if pgi variations are linked to other genomic features

  • The population genomics study of S. medicae revealed that homologous recombination has less impact on chromosomal polymorphism patterns than on plasmid-borne genes

  • Some genomic regions show evidence of directional selection, particularly around symbiosis genes

  • Methodology: Whole-genome sequencing, linkage disequilibrium analysis

All S. medicae isolates analyzed in the population genomics study fell into distinct classes with specific genetic characteristics . This classification could potentially correlate with variations in the pgi gene and symbiotic performance, providing insights into the adaptation of S. medicae strains to different environmental conditions and host genotypes.

How can S. medicae PGI data be integrated into genome-scale metabolic models?

Integrating Sinorhizobium medicae PGI data into genome-scale metabolic models requires a sophisticated approach that combines experimental biochemical data with computational modeling techniques. The methodology described below draws inspiration from the genome-scale metabolic modeling approach used for S. meliloti :

• Enzymatic data collection and curation:

  • Generate comprehensive kinetic characterization of S. medicae PGI (Km, Vmax, substrate specificity, inhibitors)

  • Determine thermodynamic equilibrium constants

  • Measure metabolic fluxes involving PGI under different conditions

  • Methodology: Combine experimental data with values from literature and databases like BRENDA

• Integration into stoichiometric models:

  • Define PGI-catalyzed reactions in the metabolic network

  • Establish flux constraints based on kinetic parameters

  • Incorporate gene expression data to further constrain the model

  • Methodology: Use tools like COBRA Toolbox for Flux Balance Analysis (FBA)

• Experimental validation:

  • Predict growth phenotypes with and without the pgi gene

  • Test predictions experimentally with mutants

  • Iteratively refine the model based on experimental results

  • Methodology: A Tn-seq-guided reconstruction process can be used to overcome limitations of using either approach in isolation

• Analysis of genetic interactions:

  • Identify synthetic lethal interactions involving pgi

  • Predict candidate genes for metabolic complementation

  • Simulate the impact of genetic variations observed in different strains

  • Methodology: In silico double gene deletion analysis, as described for S. meliloti

Note: Table values are representative and would need to be determined through experimental and computational analyses.

The integrated experimental and computational approach employed here provides unique insights into the pervasive genetic interactions that may exist across essential and accessory replicons of S. medicae , offering a consolidated view of the core metabolism of this organism.

What experimental strategies can be used to investigate interactions between PGI and other central metabolic enzymes in S. medicae?

Investigating interactions between glucose-6-phosphate isomerase (PGI) and other central metabolic enzymes in Sinorhizobium medicae requires sophisticated experimental approaches that span biochemical, genetic, and systems biology techniques:

• Protein-protein interaction analysis:

  • Co-immunoprecipitation (Co-IP): Use PGI-specific antibodies to precipitate associated protein complexes

  • Bacterial two-hybrid system (BACTH): Detect direct interactions between PGI and other metabolic enzymes

  • In vivo chemical crosslinking followed by mass spectrometry: Capture transient interactions

  • Fluorescence resonance energy transfer (FRET) microscopy: Visualize interactions in living cells

  • Methodology detail: Fuse PGI and potential partners with FRET-compatible fluorescent proteins, followed by quantitative microscopy analysis

• Metabolic flux analysis:

  • Non-stationary 13C metabolic flux analysis: Measure fluxes through PGI-involved pathways

  • Targeted metabolomics: Quantify metabolites upstream and downstream of PGI

  • Real-time in vivo flux measurement with biosensors: Monitor metabolic changes dynamically

  • Methodology detail: Feed cultures with 13C-labeled glucose, sample at short intervals, analyze by LC-MS/MS, and perform computational modeling

• Genetic interaction approaches:

  • Synthetic lethality screening: Identify genes whose deletion combined with pgi deletion is lethal

  • Suppressor mutations: Find mutations that restore growth of pgi mutants

  • CRISPR interference (CRISPRi) for fine-tuned regulation: Modulate expression of pgi and other metabolic genes

  • Methodology detail: Construct a Tn-seq library as described for S. meliloti , followed by screening for phenotypes under different growth conditions

• Structural analysis and modeling:

  • X-ray crystallography of enzyme complexes: Determine the structure of PGI in complex with other proteins

  • Molecular dynamics simulation: Model protein-protein interactions

  • Molecular docking: Predict interaction interfaces

  • Methodology detail: Purify protein complexes, crystallize in the presence of substrates/inhibitors, collect diffraction data, and solve structures

Note: Table values are representative and would need to be determined experimentally.

These integrated approaches would not only map PGI interactions with other enzymes but also provide insights into how these interactions modulate metabolic flux and contribute to the metabolic plasticity of S. medicae under different environmental conditions, particularly during symbiotic relationships with host plants.