Recombinant Lactobacillus plantarum Phosphoribosylformylglycinamidine synthase 1 (purQ)

What is the biological function of Phosphoribosylformylglycinamidine Synthase 1 (purQ) in bacteria?

Phosphoribosylformylglycinamidine Synthase 1 (purQ) is a critical component of the phosphoribosylformylglycinamidine synthase complex involved in the purine biosynthetic pathway. Within this complex, purQ specifically catalyzes the conversion of glutamine to glutamate, producing an ammonia molecule in the process . This ammonia is then utilized by another subunit, purL, which transfers it to formylglycinamide ribonucleotide (FGAR) to form formylglycinamidine ribonucleotide (FGAM) in an ATP-dependent reaction . A third component, purS, facilitates this process by interacting with both purQ and purL, assisting in the transfer of the ammonia molecule between them .

Why is Lactobacillus plantarum chosen as an expression system for recombinant proteins?

Lactobacillus plantarum has emerged as a valuable expression system for recombinant proteins due to several advantageous characteristics. As demonstrated in related research with argininosuccinate synthase expression, L. plantarum strains carrying recombinant genes can exhibit enhanced growth performance compared to control strains, particularly under stress conditions . This suggests that L. plantarum not only tolerates the expression of heterologous proteins but may also benefit from certain recombinant expressions under specific environmental conditions. Additionally, L. plantarum is a Generally Recognized As Safe (GRAS) organism with established protocols for genetic manipulation, making it suitable for research applications where biosafety is a consideration.

What expression vectors are commonly used for recombinant protein production in Lactobacillus plantarum?

For recombinant protein expression in Lactobacillus plantarum, several expression vectors have been successfully employed, with pMG36e being particularly well-documented. In analogous research involving argininosuccinate synthase, the pMG36e vector was effectively used to create the recombinant strain L. plantarum SL09 (pMG36e argG) . When designing expression systems for purQ in L. plantarum, researchers should consider vectors with strong, constitutive promoters or inducible systems depending on the experimental requirements. Selection markers compatible with Lactobacillus species, such as erythromycin resistance genes, are also critical components of these expression vectors to facilitate the identification and maintenance of transformed strains.

Experimental Design for purQ Research

Confirmation of successful purQ cloning and expression in L. plantarum requires a multi-level verification approach. For gene insertion verification, colony PCR using primers specific to both the vector and insert can confirm the presence of the correctly sized recombinant construct. This should be followed by sequencing to verify the absence of mutations.

For expression confirmation, RT-qPCR provides a sensitive method to detect and quantify purQ mRNA levels. Based on comparable research protocols, total RNA should be extracted using appropriate kits (such as RNAprep pure Cell/Bacteria Kit), verified for quality on agarose gel, and quantified spectrophotometrically . cDNA synthesis followed by qPCR with purQ-specific primers allows for relative quantification using the 2^-ΔΔCT method, with 16S rRNA serving as an effective housekeeping gene for normalization .

Additionally, enzymatic activity assays specific to purQ function (glutamine amidotransferase activity) provide functional confirmation of the expressed protein. Western blotting with antibodies against purQ or against an epitope tag engineered into the recombinant protein can provide further verification of protein expression.

What experimental controls are necessary when studying recombinant purQ in Lactobacillus plantarum?

• Negative control strain: L. plantarum transformed with the empty expression vector (e.g., pMG36e without purQ)

• Positive control: A previously characterized recombinant strain expressing a different but well-studied protein in L. plantarum

• Technical controls: For RT-qPCR, include no-template controls and no-reverse transcriptase controls

• Biological replicates: Minimum triplicate biological samples for each experimental condition to enable statistical analysis

• Growth condition controls: Standardized growth conditions with precisely defined media compositions and environmental parameters

• Time-point controls: Sampling at consistent growth phases to control for growth-dependent expression variations

These controls help isolate the effects specifically attributable to purQ expression from other variables that might influence experimental outcomes.

How can I optimize codon usage for efficient purQ expression in Lactobacillus plantarum?

Optimizing codon usage for efficient purQ expression in L. plantarum requires careful bioinformatic analysis and molecular design. Begin by analyzing the native codon usage pattern of highly expressed genes in L. plantarum using codon usage databases or tools like the Codon Usage Database. Compare this with the natural codon usage in the source organism of your purQ gene to identify potentially problematic codons.

For synthesis of your optimized purQ gene, replace rare codons in the original sequence with synonymous codons that are preferred in L. plantarum, while maintaining the same amino acid sequence. Pay particular attention to:

• Avoiding rare codons that might cause translational pausing

• Optimizing the GC content to match that of L. plantarum

• Eliminating potential secondary structures in the mRNA that could impede translation

• Removing or modifying sequence elements that resemble transcription terminators or RNase cleavage sites

After synthesizing the codon-optimized gene, compare expression levels between the native and optimized sequences through quantitative protein assays and activity measurements to confirm improved expression efficiency.

What strategies can address protein folding issues when expressing recombinant purQ in Lactobacillus plantarum?

Addressing protein folding challenges for recombinant purQ in L. plantarum requires multiple complementary strategies. Consider implementing these approaches:

• Co-expression with molecular chaperones native to L. plantarum to assist in proper protein folding

• Temperature modulation during expression—lower growth temperatures (15-25°C) often reduce aggregation and improve folding of complex proteins

• Use of fusion tags known to enhance solubility, such as thioredoxin (Trx) or NusA tags

• Expression as a fusion with native L. plantarum secretion signals to direct the protein through the secretory pathway, which may provide additional folding assistance

• Optimization of induction conditions if using inducible promoters—slower induction with lower inducer concentrations often improves folding

• Supplementation of growth media with osmolytes or folding aids such as glycine betaine or proline

For each potential solution, conduct comparative experiments measuring both protein quantity (via Western blotting) and quality (via activity assays) to determine the most effective approach for your specific construct.

How can I design experiments to study interactions between purQ and other components of the phosphoribosylformylglycinamidine synthase complex in Lactobacillus plantarum?

Studying protein-protein interactions between purQ and other components of the phosphoribosylformylglycinamidine synthase complex (purL and purS) requires specialized experimental designs. Based on current understanding of this complex, purQ produces ammonia by converting glutamine to glutamate, purL transfers this ammonia to FGAR, and purS facilitates the transfer between purQ and purL .

To investigate these interactions in a recombinant L. plantarum system, consider these methodological approaches:

• Co-immunoprecipitation (Co-IP) using antibodies against one component to pull down interaction partners

• Bacterial two-hybrid systems adapted for use in Lactobacillus

• Fluorescence resonance energy transfer (FRET) using fluorescently tagged proteins to visualize interactions in vivo

• Split-reporter systems where protein fragments reconstitute a functional reporter when interaction brings them together

• Cross-linking followed by mass spectrometry to identify interaction interfaces

Design experimental controls carefully, including:

• Negative controls with non-interacting proteins

• Positive controls with known interacting proteins

• Validation across multiple interaction detection methods

When analyzing results, focus on both qualitative evidence of interaction and quantitative measures of interaction strength under varying conditions.

What statistical approaches are recommended for analyzing growth differences between purQ-expressing and control Lactobacillus plantarum strains?

Ensure all experiments include at least triplicate biological replicates for each condition to enable meaningful statistical analysis . For growth curve analysis, consider these parameters:

• Lag phase duration

• Maximum growth rate (μmax)

• Maximum optical density reached

• Area under the curve (AUC) as an integrated measure of growth

Before applying parametric tests like ANOVA, verify that your data meets the assumptions of normality and homogeneity of variance. If these assumptions are violated, consider non-parametric alternatives such as the Kruskal-Wallis test followed by appropriate post-hoc comparisons. Statistical analysis software like SPSS can be used for these analyses .

How should I approach data validation when confronted with inconsistent results in purQ expression studies?

When confronted with inconsistent results in purQ expression studies, a systematic approach to data validation is necessary. Begin by examining possible sources of variability:

• Biological variation: Test whether inconsistencies are due to strain-specific differences or biological variability by increasing the number of biological replicates

• Technical variation: Evaluate and standardize protocols for cell growth, RNA extraction, protein purification, and activity assays

• Environmental factors: Control for batch effects in media preparation, incubation conditions, and sampling procedures

• Methodological issues: Compare results across different quantification methods (e.g., RT-qPCR vs. Western blotting vs. activity assays)

For large datasets with potential data quality issues, implement structured data validation protocols similar to those used in database research :

• Standardize records across diverse experimental structures

• Validate the accuracy and consistency of extracted data

• Establish clear criteria for inclusion/exclusion of outlier data points

• Document all data cleaning steps transparently

When reporting results with inconsistencies, clearly communicate the patterns in the data, including both consistent and inconsistent findings, along with possible explanations for the observed variability.

What bioinformatics tools are most useful for analyzing purQ sequence conservation and predicting functional domains in Lactobacillus species?

For comprehensive analysis of purQ sequence conservation and functional domain prediction across Lactobacillus species, a strategic combination of bioinformatics tools is recommended. Begin with sequence retrieval from databases such as UniProt and NCBI for purQ sequences from multiple Lactobacillus species and other bacteria with characterized purQ proteins.

For sequence conservation analysis:

• Multiple sequence alignment tools such as MUSCLE, Clustal Omega, or T-Coffee

• Conservation visualization using tools like WebLogo or Jalview

• Phylogenetic analysis using methods such as Maximum Likelihood or Bayesian inference implemented in software like MEGA, RAxML, or MrBayes

For functional domain prediction:

• InterProScan for comprehensive domain prediction combining multiple databases

• HMMER for hidden Markov model-based domain identification

• NCBI Conserved Domain Database (CDD) search

• Structure prediction using AlphaFold or RoseTTAFold to infer functional domains based on structural features

For substrate binding and catalytic site prediction:

• ConSurf for evolutionary conservation-based functional site prediction

• 3DLigandSite for ligand binding site prediction

• COACH for protein-ligand binding site prediction

Integration of these analyses can provide a comprehensive understanding of purQ structure-function relationships across Lactobacillus species, informing targeted mutagenesis experiments to validate computational predictions.

What methodologies are recommended for optimizing purQ activity in recombinant Lactobacillus plantarum for purine biosynthesis studies?

Optimizing purQ activity in recombinant L. plantarum requires a multi-faceted approach targeting gene expression, protein production, and functional activity. Begin with promoter optimization, comparing constitutive promoters of varying strengths with inducible systems to identify the optimal expression control mechanism.

For protein production optimization:

• Test different signal peptides if secretion is desired

• Optimize the ribosome binding site sequence and spacing

• Consider co-expression with folding chaperones

• Evaluate the impact of fusion tags on both expression and activity

For functional activity optimization:

• Supplement growth media with rate-limiting substrates or cofactors

• Optimize growth conditions (temperature, pH, aeration) specifically for purQ activity

• Consider metabolic engineering approaches to increase flux through connected pathways

• Evaluate the co-expression of other components of the FGAM synthase complex (purL and purS)

Implement a Design of Experiments (DOE) approach to efficiently test multiple parameters simultaneously and identify optimal conditions and potential interaction effects between variables. Activity measurements should use purified enzyme when possible, with kinetic characterization under varying substrate and cofactor concentrations.

How can researchers address data heterogeneity challenges when comparing purQ function across different Lactobacillus strains and experimental conditions?

Addressing data heterogeneity challenges when comparing purQ function across different Lactobacillus strains and experimental conditions requires systematic approaches to data standardization and integration. Researchers frequently encounter challenges similar to those identified in database research, including structural variations in data formats, heterogeneous data periods, ambiguity in records, and computational time constraints .

To address these challenges:

• Establish standardized experimental protocols and data reporting formats for:

  • Growth conditions and media compositions

  • Expression measurement methodologies

  • Activity assay conditions

  • Data presentation formats

• Implement data normalization strategies:

  • Use internal standards across experiments

  • Employ relative rather than absolute measurements where appropriate

  • Consider batch effect correction methods

• Develop robust metadata frameworks to capture:

  • Strain information (source, genotype, verification)

  • Experimental conditions (temperature, pH, media composition)

  • Measurement techniques and equipment specifications

  • Data processing steps

• Apply appropriate statistical methods for heterogeneous data:

  • Meta-analysis techniques for combining results across studies

  • Bayesian hierarchical models to account for strain and condition variability

  • Machine learning approaches for identifying patterns in complex datasets