Recombinant Bacteroides thetaiotaomicron Phosphoribosylaminoimidazole-succinocarboxamide synthase (purC)

What is the biological function of Phosphoribosylaminoimidazole-succinocarboxamide synthase (purC) in Bacteroides thetaiotaomicron?

Phosphoribosylaminoimidazole-succinocarboxamide synthase (purC) in Bacteroides thetaiotaomicron is an enzyme involved in the de novo purine biosynthesis pathway. It catalyzes the ATP-dependent conversion of 5-aminoimidazole-4-carboxyribonucleotide (AICAR) and aspartate to 5-aminoimidazole-4-(N-succinocarboxamide)-ribonucleotide (SAICAR), representing the eighth step in the purine biosynthetic pathway. This reaction is critical for nucleotide synthesis and cellular replication in B. thetaiotaomicron, which is an abundant commensal in the human gut microbiome . The enzyme belongs to the larger ATP-grasp superfamily of ligases and plays a fundamental role in the metabolism of this bacterium, which has evolved extensive metabolic versatility to thrive in the competitive gut environment .

What is known about the genomic context of the purC gene in B. thetaiotaomicron?

The purC gene in Bacteroides thetaiotaomicron is part of the purine biosynthesis gene cluster in the genome of strain ATCC 29148 (also known as VPI-5482). Based on comprehensive transcriptomic analysis, purC expression levels vary across different growth conditions, suggesting regulation in response to environmental cues . The gene is likely organized in an operon structure with other purine biosynthesis genes, although specific transcriptional start sites (TSSs) for purC have been mapped through differential RNA sequencing techniques . Unlike the polysaccharide utilization loci (PULs) that comprise approximately 20% of the B. thetaiotaomicron genome and are subject to complex regulation including phase variation, the purC gene appears to be constitutively expressed under most growth conditions as it belongs to the core metabolic machinery . Researchers should note that the genomic context can be explored further using the updated annotations available in the Theta-Base browser (http://micromix.helmholtz-hiri.de/bacteroides/)[2].

How does B. thetaiotaomicron compare to other gut bacteria in terms of purine metabolism?

Bacteroides thetaiotaomicron possesses a complete de novo purine biosynthesis pathway, including the purC gene, which is characteristic of its metabolic self-sufficiency. Unlike some gut bacteria that rely on purine salvage pathways or environmental sources, B. thetaiotaomicron can synthesize purines independently, contributing to its resilience in the competitive gut environment . While most gut bacteria possess purine metabolism capabilities, B. thetaiotaomicron's extensive metabolic versatility—evidenced by its nearly 100 polysaccharide utilization loci (PULs)—provides it with advantages in nutrient acquisition that may indirectly support nucleotide synthesis pathways . The bacterium's ability to scavenge multiple carbon sources potentially provides metabolic precursors for purine synthesis, especially during nutrient limitation conditions. Transcriptomic studies have shown that purine biosynthesis gene expression patterns in B. thetaiotaomicron change in response to different carbon sources and stress conditions, suggesting sophisticated regulatory mechanisms that may differ from other gut commensals .

What are the optimal conditions for recombinant expression of B. thetaiotaomicron purC?

For optimal recombinant expression of B. thetaiotaomicron purC, a codon-optimized construct in a pET-based vector system with T7 promoter is typically recommended for E. coli hosts. The BL21(DE3) or Rosetta(DE3) strains are preferred when expressing proteins from organisms with different codon usage patterns like B. thetaiotaomicron. Induction should be performed at mid-log phase (OD600 = 0.6-0.8) with 0.2-0.5 mM IPTG at lower temperatures (16-20°C) for 16-18 hours to enhance soluble protein yield. Including a His6-tag at either terminus facilitates purification while maintaining enzyme activity. Supplementing the growth medium with additional zinc ions (10-20 μM ZnCl2) may improve proper folding, as purC is known to require metal cofactors for structural stability. Expression trials should include multiple buffer conditions (pH 7.0-8.0) with varying salt concentrations (100-300 mM NaCl) to determine optimal solubility conditions. Researchers should monitor cell growth carefully, as excessive metabolic burden from overexpression can lead to inclusion body formation, particularly given that purC is a 314 amino acid protein with potential for complex folding requirements .

What purification strategy yields the highest purity and activity for recombinant purC?

A multi-step purification strategy is recommended to achieve high purity and preserve activity of recombinant B. thetaiotaomicron purC. Begin with immobilized metal affinity chromatography (IMAC) using a Ni-NTA column with a binding buffer containing 50 mM Tris-HCl (pH 8.0), 300 mM NaCl, 10 mM imidazole, and 5% glycerol. After binding, employ a gradient elution with increasing imidazole concentration (20-250 mM) to minimize co-purification of contaminating proteins. For further purification, size exclusion chromatography using a Superdex 200 column equilibrated with 25 mM HEPES (pH 7.5), 150 mM NaCl, and 1 mM DTT is effective for separating monomeric purC from aggregates and other impurities. If higher purity is required, an intermediate ion exchange chromatography step may be included, typically using a Q-Sepharose column at pH 8.0 with a 0-500 mM NaCl gradient. Throughout purification, maintain protein solutions at 4°C and include 1 mM DTT to prevent oxidation of cysteine residues. Activity assays should be performed after each purification step to monitor retained enzymatic function. The final purified enzyme should be stored in a buffer containing 25 mM HEPES (pH 7.5), 150 mM NaCl, 1 mM DTT, and 10% glycerol at -80°C for long-term storage or at -20°C for shorter periods .

How can one assess the proper folding and oligomeric state of purified recombinant purC?

To assess proper folding and oligomeric state of purified recombinant purC from B. thetaiotaomicron, employ a combination of biophysical and biochemical techniques. Begin with size exclusion chromatography coupled with multi-angle light scattering (SEC-MALS) to accurately determine the molecular weight and oligomeric state in solution, as purC may exist as a monomer based on available structural models . Circular dichroism (CD) spectroscopy should be performed in the far-UV range (190-260 nm) to evaluate secondary structure content and in the near-UV range (250-350 nm) to assess tertiary structure integrity. Thermal shift assays using differential scanning fluorimetry (DSF) with SYPRO Orange can determine protein stability and identify optimal buffer conditions by analyzing melting temperature (Tm) values. Dynamic light scattering (DLS) provides information about sample homogeneity and potential aggregation. For activity-based assessment, enzymatic assays monitoring the conversion of AICAR to SAICAR in the presence of ATP and aspartate can confirm functional folding. Native PAGE can be compared with SDS-PAGE to assess oligomeric state. Limited proteolysis with trypsin or chymotrypsin followed by mass spectrometry analysis can identify stable domains and flexible regions, providing insights into protein folding. These combined approaches generate a comprehensive profile of recombinant purC structural integrity prior to detailed functional characterization .

What are the kinetic parameters of recombinant B. thetaiotaomicron purC and how do they compare to the enzyme from other organisms?

The kinetic parameters of recombinant B. thetaiotaomicron purC can be determined through steady-state kinetic analysis using a coupled enzymatic assay or direct measurement of SAICAR formation. For comprehensive characterization, researchers should determine Km values for all three substrates (AICAR, aspartate, and ATP), the kcat (turnover number), and the catalytic efficiency (kcat/Km). A standard assay would monitor the reaction at 37°C in buffer containing 50 mM HEPES (pH 7.5), 10 mM MgCl2, and 1 mM DTT. When varying AICAR concentration (0.01-1 mM), hold ATP and aspartate constant at saturating concentrations (typically 2-5 mM). Similarly, when determining Km for ATP or aspartate, maintain the other substrates at saturating levels. The reaction can be monitored spectrophotometrically by coupling to other enzymes or using HPLC to detect SAICAR formation. Product inhibition studies should be conducted to determine if feedback regulation occurs. pH-rate profiles (pH 6.0-9.0) and temperature-activity relationships (15-50°C) will provide insights into the enzyme's optimal conditions. Unlike purC from E. coli or Salmonella, which have been extensively characterized, the B. thetaiotaomicron enzyme may exhibit distinct kinetic properties reflecting adaptation to the gut environment, potentially including adaptations to fluctuating pH and varying substrate availability conditions typical of the intestinal habitat .

How does the expression of purC in B. thetaiotaomicron respond to different environmental conditions?

The expression of purC in B. thetaiotaomicron demonstrates complex regulation patterns in response to environmental conditions, as revealed by transcriptomic analysis across 15 different growth conditions . During carbon source limitation or starvation, purine biosynthesis genes including purC show altered expression patterns compared to growth in nutrient-rich media, reflecting metabolic adaptation mechanisms. When B. thetaiotaomicron is exposed to stress conditions such as bile salts, antibiotics like gentamicin, or temperature shifts, specific transcriptional responses occur that may affect purC expression as part of broader metabolic reprogramming . Notably, growth on host-derived substrates like mucin appears to trigger significant transcriptional changes, indicating that purC expression may be influenced by the nutritional landscape of the gut environment . The transcriptional regulation of purC likely involves both specific regulatory factors responding to purine availability and global regulators such as the carbon utilization regulator (Cur), which orchestrates metabolic adaptations to carbon source availability . Researchers can access condition-specific expression data for purC through the Theta-Base web browser (http://micromix.helmholtz-hiri.de/bacteroides/), which provides a valuable resource for understanding contextual regulation of this enzyme in B. thetaiotaomicron .

How can recombinant purC be used to study B. thetaiotaomicron metabolism and host-microbe interactions?

Recombinant purC from B. thetaiotaomicron can serve as a powerful tool for investigating microbial metabolism and host-microbe interactions through multiple experimental approaches. Researchers can develop biochemical assays using the purified enzyme to screen for novel inhibitors, which can then be employed as chemical probes to dissect purine metabolism in live bacteria without causing general growth inhibition. By creating a conditional knockout strain where purC expression can be modulated, researchers can investigate how purine biosynthesis disruption affects B. thetaiotaomicron's ability to colonize the gut in gnotobiotic mouse models. Isotope-labeled metabolic flux analysis combined with purC enzymatic assays can reveal how carbon sources differentially contribute to nucleotide biosynthesis under various dietary conditions . The enzyme can be employed in structural studies to identify allosteric regulation mechanisms that might be exploited by the bacterium to adapt to changing environmental conditions in the gut. Co-culture experiments with host cells (intestinal epithelial cells) and purC-modulated B. thetaiotaomicron strains can determine how bacterial purine metabolism influences host cell responses, including inflammation and barrier function . Additionally, the enzyme can be used to develop biosensors for in vivo monitoring of purine metabolism dynamics in complex microbial communities, providing insights into metabolic interactions within the gut microbiome .

What approaches can be used to investigate the role of purC in B. thetaiotaomicron colonization and persistence in the gut?

Investigating the role of purC in B. thetaiotaomicron colonization and persistence requires a multi-faceted approach combining genetic manipulation, animal models, and advanced analytical techniques. Researchers should first generate a conditional knockout strain using CRISPR-Cas9 or transposon mutagenesis systems where purC expression can be precisely controlled by inducible promoters. This strain can then be used in gnotobiotic mouse colonization experiments to assess establishment, persistence, and competitive fitness against wild-type strains or other gut commensals . Metatranscriptomic analysis of gut contents can monitor in vivo expression of purC under different dietary regimens, particularly those that alter available carbon sources, creating varying metabolic demands . Metabolomic profiling of gut contents from mice colonized with wild-type versus purC-modified strains can reveal altered metabolite profiles extending beyond purines, potentially uncovering unexpected metabolic network connections. Stable isotope probing experiments using 15N-labeled precursors can track purine biosynthesis flux in vivo. Dual RNA-seq approaches can simultaneously monitor host and bacterial transcriptional responses to purC modulation, providing insights into host-microbe dialogue . Finally, spatial distribution of colonization can be assessed using fluorescence in situ hybridization (FISH) with purC mutant strains to determine if purine biosynthesis affects bacterial localization within the gut ecosystem, particularly in relation to the mucus layer where B. thetaiotaomicron is known to utilize host-derived substrates .

What are common challenges in recombinant expression of B. thetaiotaomicron proteins and how can they be addressed?

Recombinant expression of B. thetaiotaomicron proteins, including purC, presents several challenges that require systematic troubleshooting. The primary challenge is codon usage bias, as B. thetaiotaomicron has a GC content of approximately 43%, differing significantly from expression hosts like E. coli. This can be addressed by either using codon-optimized synthetic genes or employing specialized E. coli strains like Rosetta that supply rare tRNAs. Protein insolubility is another common issue, often requiring optimization of induction conditions—lowering IPTG concentration to 0.1-0.2 mM and induction temperature to 16-18°C can significantly improve soluble yields. Including solubility-enhancing fusion tags like SUMO, MBP, or TrxA can help, provided they can be cleanly removed post-purification. B. thetaiotaomicron proteins may contain disulfide bonds or require specific cofactors for proper folding; supplementing growth media with appropriate cofactors and using E. coli strains with enhanced disulfide bond formation capabilities (like SHuffle) can improve functional yields. Proteolytic degradation can be minimized by including protease inhibitors throughout purification and using E. coli strains deficient in certain proteases. For proteins that remain challenging, cell-free expression systems or expression in alternative hosts like yeast or insect cells might be necessary. Finally, expression conditions should be systematically optimized through small-scale expression trials varying media composition, induction timing, and harvest times before scaling up .

How can enzymatic assays for purC be optimized for high-throughput screening applications?

Optimizing purC enzymatic assays for high-throughput screening requires adapting the traditional assay to microplate format while ensuring sensitivity, reproducibility, and stability. Begin by developing a coupled spectrophotometric assay where SAICAR formation is linked to NAD(P)H oxidation through auxiliary enzymes, allowing continuous monitoring at 340 nm in 96 or 384-well plates. Alternatively, design a fluorescence-based assay using fluorescently-labeled substrates or coupling product formation to a fluorogenic reaction. For stability, formulate an optimized assay buffer (typically 50 mM HEPES pH 7.5, 10 mM MgCl2, 1 mM DTT, 0.01% Triton X-100) that minimizes enzyme degradation during automated handling. Determine the linear range of the assay with respect to both time and enzyme concentration to establish appropriate reaction conditions. Miniaturize the reaction volume to 50-100 μL for 96-well or 15-30 μL for 384-well formats while adjusting substrate concentrations to approximately 1-2 times Km values for balanced sensitivity to competitive inhibitors. Implement internal controls including positive controls (known inhibitors), negative controls (no enzyme), and DMSO controls (at screening concentration) in each plate. Evaluate assay quality using Z′-factor determination, aiming for values >0.7 for robust screening. For endpoint assays, optimize quenching conditions that effectively stop the reaction without interfering with detection. Finally, validate the optimized assay by screening a small diversity set of compounds and confirming hits with orthogonal secondary assays to eliminate false positives from assay interference.

What strategies can overcome expression variability when producing recombinant purC for structural studies?

Producing recombinant B. thetaiotaomicron purC with consistent quality for structural studies requires addressing expression variability through systematic optimization. Begin by constructing multiple expression constructs with different fusion tags (His6, MBP, GST) at both N- and C-termini, including precision protease cleavage sites for tag removal. Test these constructs in parallel using small-scale expression trials. Once the optimal construct is identified, standardize the expression protocol by preparing master glycerol stocks and using a single colony for each expression batch to ensure genetic uniformity. Employ auto-induction media which provides more consistent expression levels than manual IPTG induction by gradually initiating protein production as cells reach appropriate density. Monitor growth curves for each batch and harvest at consistent OD600 values to ensure reproducibility. For structural studies, protein purity is critical—implement a three-step purification protocol combining IMAC, ion exchange, and size exclusion chromatography with stringent quality control checkpoints between each step. Analyze each purified batch using dynamic light scattering to confirm monodispersity and absence of aggregation. Standardize buffer composition through systematic screening of buffer conditions using thermal shift assays to identify formulations that maximize protein stability. For crystallization purposes, concentrate protein immediately before setting up trials, avoiding freeze-thaw cycles. Implement batch validation using activity assays and circular dichroism to ensure consistent structural integrity between preparations. Finally, consider limited proteolysis followed by mass spectrometry to identify stable domains if full-length protein proves recalcitrant to crystallization .

What statistical approaches are most appropriate for analyzing purC enzyme kinetics data?

For robust analysis of purC enzyme kinetics data, researchers should implement a multi-tiered statistical approach. Initial data analysis should include non-linear regression using the appropriate enzyme kinetics model (Michaelis-Menten, Hill equation, or substrate inhibition models) with software such as GraphPad Prism or R with the drc package. For each kinetic parameter (Km, Vmax, kcat), calculate 95% confidence intervals rather than relying solely on standard error values. Evaluate goodness-of-fit using both R² values and residual plots to detect systematic deviations from the model. When comparing kinetic parameters between different experimental conditions (pH, temperature, or mutant variants), utilize global fitting approaches that can directly test whether parameters differ significantly between conditions. For complex kinetic mechanisms involving multiple substrates, apply model discrimination approaches using Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) to determine the most appropriate model (random, ordered, or ping-pong mechanisms). Implement bootstrapping methods (1000+ resamples) to generate robust parameter estimates that don't rely on assumptions about error distribution. For reproducibility assessment, calculate intra-assay and inter-assay coefficients of variation (CV), aiming for values below 10% and 15%, respectively. When analyzing inhibition data, fit to competitive, non-competitive, or mixed inhibition models and statistically compare these fits. Finally, for temperature-dependent kinetics, apply Eyring-Polanyi analysis to extract thermodynamic activation parameters, providing deeper insights into catalytic mechanism.

How can structural bioinformatics tools be applied to predict functional regions in B. thetaiotaomicron purC?

Structural bioinformatics offers powerful approaches for predicting functional regions in B. thetaiotaomicron purC prior to experimental validation. Begin with multiple sequence alignment (MSA) of purC homologs across diverse bacterial species using MUSCLE or MAFFT algorithms to identify highly conserved residues that likely participate in catalysis or substrate binding. Apply ConSurf or Evolutionary Trace analysis to map conservation scores onto existing structural models, revealing evolutionary pressure on specific residues . Utilize automated active site prediction servers like CASTp or POCASA to identify potential binding pockets based on the SWISS-MODEL structure, with special attention to cavities that match the expected dimensions for AICAR, aspartate, and ATP binding . For substrate docking predictions, employ molecular docking tools like AutoDock Vina or HADDOCK with the modeled structure to predict binding modes of substrates and identify key interacting residues. Molecular dynamics simulations can reveal flexible regions and conformational changes that might be important for catalysis. Analyze electrostatic surface potentials using APBS to identify regions likely involved in substrate recognition, particularly for the negatively charged ATP and AICAR molecules. Network analysis approaches like protein structure networks (PSN) can identify allosteric communication pathways between distal sites in the protein. Coevolution analysis using methods like GREMLIN or EVcouplings can predict residue pairs that maintain contact, providing insights into structural constraints. These computational predictions should then inform the design of site-directed mutagenesis experiments targeting high-confidence predicted functional residues, creating a virtuous cycle between computational prediction and experimental validation .