Recombinant Staphylococcus aureus Phosphoribosylglycinamide formyltransferase (purN)

What is the biochemical function of purN in S. aureus metabolism?

PurN (phosphoribosylglycinamide formyltransferase) catalyzes the conversion of glycinamide ribonucleotide (GAR) to formylglycinamide ribonucleotide (fGAR), representing a critical step in the de novo purine biosynthesis pathway. This reaction uses 10-formyltetrahydrofolate as the formyl donor and is essential for the production of inosine monophosphate (IMP), which serves as a precursor for both adenine and guanine nucleotides. The enzyme forms part of a coordinated metabolic network that influences not just nucleotide pools but also energy metabolism, amino acid utilization, and ultimately bacterial survival under stress conditions .

How does purN contribute to antibiotic tolerance in S. aureus?

PurN plays a significant role in the formation of antibiotic-tolerant persister cells in S. aureus, particularly during the late exponential growth phase. Deletion mutants (ΔpurN) show significantly increased susceptibility to antibiotics compared to wild-type strains. When exposed to ampicillin, ΔpurN cells from 5-hour cultures are completely killed after 3 days, while wild-type bacteria maintain approximately 10^6 CFU/mL of viable cells. Even after 10 days of ampicillin treatment, wild-type strains retain approximately 10^2 CFU/mL of surviving bacteria . Similar patterns occur with levofloxacin exposure, with the most significant differences observed in 5-hour cultures. This growth phase specificity suggests that purN's contribution to antibiotic tolerance is linked to metabolic adaptations occurring during the transition to stationary phase .

What relationship exists between purN and S. aureus virulence?

PurN significantly influences S. aureus virulence through multiple mechanisms. Research demonstrates that purN deletion affects virulence gene expression, hemolytic ability, and biofilm formation. In animal models, the LD50 of the ΔpurN mutant (3.28 × 10^10 CFU/mL) is approximately 10 times higher than that of the wild-type strain (2.81 × 10^9 CFU/mL), indicating substantially reduced virulence . The molecular basis for this virulence attenuation involves activation of the SaeRS two-component system, which regulates numerous virulence factors in S. aureus. This connection between a metabolic enzyme and virulence regulation highlights the sophisticated integration of basic metabolism with pathogenicity mechanisms in this bacterial pathogen .

What are effective approaches for generating purN knockout mutants in S. aureus?

Creating precise purN knockout mutants in S. aureus requires a methodical approach:

• Design primers that amplify approximately 1kb regions upstream and downstream of the purN gene

• Use fusion PCR or restriction-ligation approaches to join these fragments, often inserting an antibiotic resistance marker between them

• Clone this construct into a temperature-sensitive vector (such as pBT2)

• Transform the plasmid into S. aureus using electroporation

• Employ temperature shifts (typically 30°C to 42°C) and antibiotic selection to promote homologous recombination and plasmid curing

• Screen potential mutants using PCR to verify gene deletion

• Confirm the absence of purN expression using RT-qPCR

• Sequence the mutated region to ensure precise deletion without affecting adjacent genes

This approach creates clean deletion mutants suitable for subsequent phenotypic analysis, complementation studies, and transcriptomic investigations .

How can researchers effectively complement purN mutations for validation studies?

Complementation studies are essential for confirming that observed phenotypes result specifically from purN deletion rather than polar effects or secondary mutations. The following methodology has proven effective:

• Amplify the complete purN coding sequence from wild-type S. aureus DNA using high-fidelity polymerase and primers containing appropriate restriction sites

• Digest the purN fragment and expression vector (e.g., pRAB11) with matching restriction enzymes (KpnI and EcoRI have been successfully used)

• Ligate the fragment into the vector and transform into E. coli DC10B for plasmid amplification and sequence verification

• Extract the verified plasmid and electrotransform into the ΔpurN mutant strain

• Include appropriate controls: empty vector in wild-type (Newman::pRAB11), empty vector in mutant (ΔpurN::pRAB11), and purN-containing vector in wild-type (Newman::pRBpurN)

• Induce expression using anhydrotetracycline (Atc) at appropriate concentrations

• Confirm restored purN expression using RT-qPCR

• Validate phenotypic complementation through antibiotic tolerance assays

What assays can quantify purN enzymatic activity in bacterial lysates?

Several methodological approaches can be employed to measure purN enzymatic activity:

When working with S. aureus lysates, researchers should include controls such as heat-inactivated samples and lysates from purN deletion strains to account for background activity and ensure assay specificity.

How do transcriptomic changes in purN mutants explain the observed phenotypes?

Transcriptome analysis reveals that purN deletion has far-reaching effects on S. aureus gene expression. In the ΔpurN mutant compared to wild-type:

• 58 genes are significantly downregulated, including those involved in:

  • Purine metabolism pathways

  • Alanine, aspartate, and glutamate metabolism

  • 2-oxocarboxylic acid metabolism

• 24 genes are significantly upregulated, primarily associated with:

  • ABC transporter systems

  • Transferase activity

These expression changes explain the multiple phenotypic alterations observed in purN mutants. The downregulation of purine metabolism genes indicates a compensatory response to the metabolic block caused by purN deletion. Changes in amino acid metabolism, particularly involving glutamate, connect to the identified relationship between purN and GltB (glutamate synthase). The altered expression of transporter systems suggests adaptive responses to metabolic imbalances, potentially affecting the import of nutrients or export of toxic metabolites .

What experimental design considerations are critical when studying purN in different growth phases?

When investigating purN's role across different growth phases, researchers must implement a carefully structured experimental design:

• Growth phase standardization: Define precise sampling points based on growth curve characteristics rather than arbitrary time points. For S. aureus, key phases include:

  • Early exponential (OD600 ~0.2-0.3)

  • Mid-exponential (OD600 ~0.5-0.7)

  • Late exponential (OD600 ~1.0-1.2)

  • Early stationary (OD600 ~1.5-1.8)

  • Late stationary (24+ hours)

• Medium composition control: Use chemically defined media to eliminate variability in nutrient availability that might mask or exaggerate purN-dependent phenotypes

• Inoculum standardization: Start cultures from colonies of similar size and age or from frozen stocks with standardized OD600 to minimize variability

• Temporal analysis design: For antibiotic tolerance testing across growth phases:

  Growth Phase	Culture Time	Sampling Points During Antibiotic Exposure	Key Controls
  Late Exponential	5 hours	0, 1, 2, 3, 5, 7, 10 days	Both wild-type and ΔpurN without antibiotic
  Early Stationary	9 hours	0, 1, 2, 3, 5, 7, 10 days	Heat-killed controls to confirm killing
  Late Stationary	18 hours	0, 1, 2, 3, 5, 7, 10 days	Media-only controls to check contamination

• Antibiotic selection: Include multiple antibiotic classes (β-lactams, fluoroquinolones, aminoglycosides, glycopeptides) to distinguish mechanism-specific from general tolerance effects

• Technical replicates: Perform plating in technical triplicates for each biological replicate to account for plating variability

• Biological replicates: Conduct at least three independent biological replicates starting from different colonies/cultures

This structured approach enables robust investigation of growth phase-dependent effects of purN on antibiotic tolerance and other phenotypes .

What methodological issues commonly arise when measuring antibiotic tolerance in purN mutants?

Several technical challenges can complicate antibiotic tolerance assays with purN mutants:

• Inoculum effect: Variations in starting bacterial density can significantly affect persister frequencies. Standardize initial inoculum precisely (within 5% variation) using spectrophotometric measurements and confirmed by plate counting.

• Antibiotic stability issues: Some antibiotics degrade during extended incubation periods. For accurate long-term tolerance assays:

  • Replace antibiotics every 48 hours with fresh solutions

  • Verify antibiotic activity using susceptible control strains

  • Store antibiotic stock solutions according to manufacturer recommendations

• Cfu counting challenges: When bacterial counts drop below 10^3 CFU/mL, direct plating becomes unreliable. Implement:

  • Large volume plating (spreading 0.5-1mL instead of standard 100μL)

  • Membrane filtration methods to concentrate cells from large volumes

  • Most probable number (MPN) techniques for extremely low concentrations

• Growth medium carryover: Antibiotic activity can be affected by components in the growth medium. Wash cells in phosphate buffer before antibiotic exposure or dilute appropriately to minimize carryover effects.

• Detection of viable but non-culturable (VBNC) cells: Standard plating may miss VBNC cells. Consider complementary approaches such as:

  • Live/dead staining with flow cytometry

  • Metabolic activity assays (e.g., resazurin reduction)

  • RNA-based viability assessments

• Spontaneous resistance development: During extended experiments, antibiotic-resistant mutants may emerge. Confirm that surviving cells remain susceptible by re-testing their MICs after recovery.

What strategies can overcome purification challenges with recombinant S. aureus purN?

Obtaining pure, active recombinant S. aureus purN protein presents several challenges that can be addressed with these methodological strategies:

• Solubility optimization:

  • Express at lower temperatures (16-20°C) to improve folding

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

  • Include solubility enhancers in lysis buffer (e.g., 5-10% glycerol, 0.1% Triton X-100)

  • Screen buffer conditions systematically using thermal shift assays

• Purification strategy optimization:

  • Implement multi-step purification combining affinity chromatography, ion exchange, and size exclusion

  • Maintain reducing conditions throughout purification (2-5 mM DTT or β-mercaptoethanol)

• Stabilizing purN activity:

  • Include enzyme substrates or substrate analogs during purification

  • Add metal ions that may be required for structural integrity (Mg2+, Mn2+)

  • Determine optimal pH range (typically 7.0-8.0) and buffer composition

  • Test additives that may enhance stability (trehalose, arginine, proline)

• Quality control metrics:

  • Assess purity by SDS-PAGE (aim for >95%)

  • Confirm identity by mass spectrometry

  • Verify activity using enzymatic assays

  • Evaluate thermal stability using differential scanning fluorimetry

  • Check oligomeric state by native PAGE or analytical size exclusion

What experimental approaches could elucidate the specific metabolic signals linking purN to virulence regulation?

Understanding the metabolic signals connecting purN activity to virulence regulation requires innovative experimental strategies:

• Metabolomic profiling:

  • Perform untargeted LC-MS-based metabolomics comparing wild-type, ΔpurN, and complemented strains

  • Conduct parallel targeted analysis focusing on purine intermediates, glutamate pathway metabolites, and potential signaling molecules

  • Implement isotope tracing with 13C-labeled carbon sources to track metabolic flux changes

  • Compare metabolite profiles during different growth phases with correlation to virulence gene expression

• Genetic screening approaches:

  • Perform transposon sequencing (Tn-seq) in purN mutant background to identify suppressors that restore virulence

  • Use CRISPR interference libraries targeting metabolic genes to identify synthetic relationships with purN

  • Create reporter strains with SaeR-dependent promoters fused to fluorescent proteins to screen metabolite libraries for activation signals

• Biochemical signal identification:

  • Develop in vitro transcription systems with purified SaeRS components to test potential metabolic intermediates as direct activators

  • Use protein-metabolite interaction screening methods (thermal shift assays, isothermal titration calorimetry) to identify direct binding partners

  • Apply pull-down approaches with immobilized SaeS sensor domain to identify interacting metabolites

• Mathematical modeling:

  • Develop kinetic models of purine metabolism integrated with amino acid metabolism

  • Predict metabolite concentration changes following purN deletion

  • Generate testable hypotheses about which metabolites serve as signals

How might purN be exploited as a potential target for anti-persister therapeutics?

PurN represents a promising target for anti-persister therapeutic strategies based on its role in antibiotic tolerance. Development pathways include:

Drug development would require:

• High-resolution structural studies of S. aureus purN

• Development of medium/high-throughput screening assays

• Medicinal chemistry optimization of lead compounds

• In vitro and in vivo efficacy testing in persister models

• Toxicity and pharmacokinetic/pharmacodynamic assessments

How should researchers analyze time-kill curves when comparing wild-type and purN mutant strains?

Proper analysis of time-kill curves requires rigorous statistical approaches and careful interpretation:

• Data transformation and visualization:

  • Plot survival data on a logarithmic scale (log10 CFU/mL vs. time)

  • Calculate percent survival relative to initial inoculum at each time point

  • Generate survival curves showing mean values with standard deviation or standard error bars

  • Consider heat maps for comparing multiple conditions simultaneously

• Statistical analysis framework:

  • Apply two-way repeated measures ANOVA to assess effects of strain type, time, and their interaction

  • Use post-hoc tests with appropriate corrections for multiple comparisons

  • Compare fitted parameters across strains and conditions using appropriate statistical tests

• Quantification metrics:

  • Calculate MDK (minimum duration of killing) values:

    • MDK99: Time required to kill 99% of the population

    • MDK99.9: Time required to kill 99.9% of the population

    • MDK99.99: Time required to kill 99.99% of the population

  • Determine persister fractions at defined time points

  • Calculate area under the killing curve (AUKC) as an integrated measure of tolerance

• Reporting standards:

  • Clearly state initial inoculum sizes with confidence intervals

  • Report both absolute counts and relative survival percentages

  • Include detection limits on all graphs

  • Specify biological and technical replicate numbers

What statistical approaches should be used when analyzing transcriptome data from purN studies?

Analysis of transcriptomic data from purN studies requires robust statistical methodology:

• Quality control and preprocessing:

  • Assess RNA quality (RIN scores >8 recommended)

  • Perform adapter trimming and quality filtering of reads

  • Map to appropriate S. aureus reference genome

  • Normalize count data to account for sequencing depth differences

• Differential expression analysis:

  • Apply negative binomial models (DESeq2 or edgeR) for RNA-seq data

  • Use moderated t-tests (limma) for microarray data

  • Implement multiple testing correction (Benjamini-Hochberg FDR)

  • Set significance thresholds (typically adjusted p<0.05 and |log2FC|>1)

• Advanced analytical approaches:

  • Perform gene set enrichment analysis (GSEA) for pathway-level effects

  • Use gene ontology (GO) enrichment to identify biological processes affected

  • Implement weighted gene co-expression network analysis (WGCNA) to identify gene modules

  • Apply causal network inference to model regulatory relationships

• Integration with other data types:

  • Correlate expression changes with metabolomic alterations

  • Map transcriptional changes to protein-protein interaction networks

  • Compare with published datasets on related conditions (antibiotic exposure, other metabolic mutants)

• Validation approaches:

  • Confirm key expression changes using RT-qPCR

  • Verify functional impacts through targeted protein assays

  • Use genetic complementation to reverse transcriptional changes

This rigorous analytical framework ensures reliable interpretation of the complex transcriptional responses to purN deletion, providing mechanistic insights into the observed phenotypic changes.