Recombinant Pseudomonas putida Coenzyme PQQ synthesis protein F (pqqF), partial

What is the role of PqqF in the PQQ biosynthesis pathway?

PqqF functions as a peptidase that processes the PqqA precursor peptide during PQQ biosynthesis. Specifically, after PqqE links the C9 and C9a in the PqqA peptide, PqqF cuts out these linked amino acids, which is a crucial early step in the pathway. PqqF is sufficient to degrade PqqA on its own, though research suggests it may form a heterodimeric complex with PqqG in some bacterial species to enhance proteolytic processing of PqqA peptides . The complete PQQ biosynthesis pathway involves five sequential reactions transforming glutamate and tyrosine residues from PqqA into the final quinone cofactor structure.

How does a pqqF mutation affect Pseudomonas putida metabolism?

Mutations in pqqF lead to significant metabolic alterations in Pseudomonas strains. In P. putida, pqqF mutants show fitness defects when grown on alcoholic substrates due to impaired function of PQQ-dependent alcohol dehydrogenases . Similarly, in P. fluorescens, pqqF mutants cannot utilize ethanol as a carbon source and exhibit altered secondary metabolite production, particularly enhanced pyoluteorin (Plt) production . These phenotypes can be reversed through complementation with functional pqq+ plasmids or by direct supplementation with 16 nM PQQ, confirming the specific role of PqqF in generating the essential PQQ cofactor .

What proteins interact with PqqF during PQQ biosynthesis?

PqqF interacts with several proteins in the PQQ biosynthesis pathway. It primarily processes the PqqA precursor peptide after initial modification by PqqE. Research from Methylorubrum extorquens suggests that PqqF may form a heterodimeric complex with PqqG to enhance the proteolytic processing of PqqA peptides, although PqqF alone is sufficient for PqqA degradation . The complete pathway involves additional proteins including PqqB (an oxidoreductase), PqqC (catalyzes final cyclization and oxidation steps), PqqD (a putative PQQ carrier protein), and PqqE (links C9 and C9a in PqqA) .

How is the pqqF gene organized in the P. putida genome?

The pqqF gene in P. putida is part of the pqq gene cluster responsible for PQQ biosynthesis. Across various bacterial species, the organization of pqq genes can differ. In Pseudomonas fluorescens, for example, the region contains three complete open reading frames designated as PqqFAB . Fitness data from P. putida shows that the genes required for PQQ biosynthesis (pqqEFC) all display significant fitness defects on alcohols when mutated, similar to pedF mutants . The genomic organization is important for understanding transcriptional regulation and potential co-expression of the PQQ biosynthesis genes.

What are the molecular mechanisms of PqqF substrate recognition and catalysis?

PqqF functions as a peptidase that specifically recognizes the PqqA precursor peptide after it has been modified by PqqE. The molecular basis for this specificity likely involves recognition of the linked C9 and C9a residues in the modified PqqA peptide . Current research suggests that PqqF belongs to a family of proteases that cleave specific peptide bonds to release the modified amino acids required for PQQ synthesis. The catalytic mechanism likely involves metal coordination, as seen in related proteases, though detailed structural studies specifically on P. putida PqqF are still emerging. Understanding the exact substrate-binding pocket and catalytic residues would require structural analysis through X-ray crystallography or cryo-EM approaches, combined with site-directed mutagenesis of putative catalytic residues.

How do trace element concentrations affect PqqF function and PQQ biosynthesis?

Trace element concentrations significantly impact PQQ biosynthesis and the function of PQQ-dependent enzymes. Research with P. putida has shown that the critical concentration of rare earth elements (REEs) that supports growth based on PQQ-dependent enzymes differs dramatically depending on the minimal medium used . For instance, in modified M9 medium, approximately 10 μM La³⁺ was necessary for PedH (a PQQ-dependent enzyme) activity, while only 20-100 nM La³⁺ was required in MP medium . This variation is attributed to differences in trace element composition between media:

These differences suggest that metal ions may compete for binding sites in PQQ-dependent enzymes or affect the biosynthesis of PQQ itself, potentially through effects on PqqF activity or other biosynthetic enzymes .

What is the relationship between PqqF, glucose metabolism, and secondary metabolite production?

The relationship between PqqF, glucose metabolism, and secondary metabolite production is complex and multifaceted. In P. fluorescens, PqqF mutations lead to enhanced production of the secondary metabolite pyoluteorin (Plt) . This suggests that PQQ-dependent processes influence the regulation of secondary metabolism. The mechanism likely involves alterations in central carbon metabolism due to the inability to utilize certain carbon sources that require PQQ-dependent dehydrogenases.

In P. putida, the deletion of gcd (glucose dehydrogenase) gene, which encodes a PQQ-dependent enzyme, affects glucose utilization and can impact co-utilization of different carbon sources like glucose and cellobiose . This indicates a potential regulatory connection between PQQ-dependent pathways and carbon catabolite repression systems. The impact of PqqF specifically on these processes would be mediated through its role in PQQ biosynthesis, as PQQ is the essential cofactor for these dehydrogenases.

How can directed evolution be applied to optimize PqqF for enhanced PQQ production?

Directed evolution of PqqF could be implemented through the following methodological approach:

• Library Creation: Generate a library of pqqF variants using error-prone PCR, DNA shuffling, or site-saturation mutagenesis focusing on residues predicted to be involved in substrate binding or catalysis.

• Selection System: Develop a high-throughput screening system based on:

  • Growth complementation assays using a pqqF-deficient strain on alcohol substrates

  • Colorimetric or fluorescent assays for PQQ detection

  • Reporter systems linking PQQ production to expression of fluorescent proteins

• Iterative Selection: Perform multiple rounds of selection under increasingly stringent conditions, potentially incorporating varying metal ion concentrations to identify variants with altered cofactor requirements.

• Characterization of Improved Variants: Analyze successful variants for:

  • Kinetic parameters (kcat, KM) using purified enzymes

  • Structural changes via X-ray crystallography or cryo-EM

  • In vivo PQQ production levels through HPLC analysis

  • Growth phenotypes on various carbon sources

• Validation: Confirm that enhanced PQQ production translates to improved performance in biotechnological applications, such as alcohol oxidation or biosensor development.

What are the optimal conditions for expressing recombinant PqqF in heterologous hosts?

For optimal expression of recombinant PqqF, researchers should consider the following methodological approach:

Expression System Selection:

• E. coli BL21(DE3) is commonly used for initial expression attempts due to its robust growth and high transformation efficiency.

• P. putida KT2440 offers a native-like environment that may improve proper folding and activity.

Expression Vector Design:

• Incorporate a strong inducible promoter (T7 for E. coli, Pm/XylS for Pseudomonas)

• Include an affinity tag (His6, FLAG, or Strep-tag) for purification

• Consider a fusion partner (MBP, SUMO) to enhance solubility if initial expression yields insoluble protein

Culture Conditions:

• Temperature: Lower temperatures (16-25°C) after induction often improve solubility

• Media: Rich media for high biomass (LB, TB) or defined media (M9) supplemented with trace elements

• Induction timing: Mid-log phase (OD600 0.6-0.8) typically yields better results

• Inducer concentration: Test a range (0.1-1.0 mM IPTG for T7 promoter) to optimize expression level versus solubility

Purification Strategy:

• Cell lysis (sonication or French press) in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol

• Affinity chromatography using tag-specific resin

• Size exclusion chromatography for final purity

• Activity assay using synthetic PqqA peptide substrate

Activity Validation:
Confirm functionality through complementation of a pqqF-deficient strain or direct measurement of peptidase activity on purified PqqA substrates.

How can researchers generate and screen pqqF mutants to study structure-function relationships?

Mutant Generation Strategies:

• Site-Directed Mutagenesis:

  • Target conserved residues identified through sequence alignment with related proteases

  • Focus on putative catalytic residues and substrate-binding regions

  • Use overlap extension PCR or commercial mutagenesis kits

• Random Mutagenesis:

  • Error-prone PCR with controlled mutation rates (2-5 mutations/kb)

  • DNA shuffling with related pqqF genes from other Pseudomonas species

• Domain Swapping:

  • Exchange domains between PqqF and related peptidases to identify specificity determinants

Screening Methods:

• In vivo Complementation Assay:

  • Transform pqqF mutants into a P. putida ΔpqqF strain

  • Screen for restoration of growth on ethanol or other alcohols requiring PQQ-dependent dehydrogenases

  • Quantify growth rates in liquid culture using a plate reader

• Biochemical Assays:

  • Express and purify mutant PqqF proteins

  • Assess peptidase activity using synthetic PqqA peptides

  • Monitor activity through HPLC detection of cleavage products or fluorescence-based assays

• PQQ Production Quantification:

  • Extract PQQ from culture supernatants

  • Quantify using HPLC with fluorescence detection

  • Calculate specific PQQ production rates normalized to biomass

Data Analysis:

• Generate structure-function maps correlating mutation positions to activity levels

• Use clustering analysis to identify functionally important residue groups

• Apply molecular modeling to interpret biochemical data in a structural context

What methods are most effective for monitoring PQQ production in recombinant P. putida strains?

Analytical Methods for PQQ Quantification:

• HPLC-Based Methods:

  • Sample preparation: Centrifuge cultures, filter supernatant (0.22 μm), concentrate if necessary

  • Chromatography conditions: C18 reverse-phase column, gradient elution with acidified water/acetonitrile

  • Detection: UV absorption (249 and 275 nm) or fluorescence detection (Ex 365 nm, Em 450 nm)

  • Quantification: External calibration curve using PQQ standard

• Enzymatic Assays:

  • Principle: Use PQQ-dependent glucose dehydrogenase (GDH) from PQQ-deficient strains

  • Reconstitution: Mix sample containing PQQ with apo-GDH

  • Activity measurement: Monitor glucose oxidation spectrophotometrically using artificial electron acceptors (DCPIP)

  • Quantification: Compare to standard curve of known PQQ concentrations

• Biosensor-Based Methods:

  • Construct reporter strains containing PQQ-dependent promoters linked to luminescence or fluorescence genes

  • Co-culture reporter strain with PQQ-producing strain

  • Measure signal intensity to quantify PQQ production

  • Example: Use pTn7-M-pedH-lux or pTn7-M-pedE-lux constructs as described in the literature

In vivo Monitoring:

• Promoter-Reporter Fusions:

  • Integrate pedH-lux or pedE-lux fusions into the chromosome

  • Measure luminescence in real-time during growth

  • Normalize to OD600 to calculate promoter activity in relative light units (RLU)

  • This method allows dynamic monitoring of PQQ-dependent gene expression

• Growth-Based Detection:

  • Monitor growth of the strain on PQQ-dependent carbon sources (alcohols)

  • Compare growth rates and yields to wild-type and standard PQQ concentrations

  • Method is less sensitive but provides functional relevance

How should researchers interpret cofitness data for pqqF and other PQQ biosynthesis genes?

Cofitness analysis provides crucial insights into functional relationships between genes. For PQQ biosynthesis genes, proper interpretation requires:

Methodological Approach to Cofitness Analysis:

• Data Collection and Normalization:

  • Generate fitness scores for each gene across multiple conditions (carbon sources, stress conditions)

  • Normalize fitness scores to account for differences in growth rates and library coverage

  • Calculate Pearson correlation coefficients between fitness profiles of different genes

• Interpretation Framework:

  • High cofitness (correlation coefficient >0.7) suggests functional relationships:

    • Genes in the same pathway

    • Protein-protein interactions

    • Regulatory relationships

  • Cluster analysis to identify functional modules

• PQQ Biosynthesis Gene Analysis:
  Research has shown that pqqF and other PQQ biosynthesis genes (pqqEFC) display significant cofitness when P. putida is grown on alcohols . This indicates that:

  • These genes function in the same pathway

  • The absence of any component severely impacts PQQ-dependent metabolism

  • The genes are likely essential for the same biochemical process

• Comparative Analysis with Related Genes:

  • Compare cofitness patterns of pqqF with PQQ-dependent dehydrogenases like pedF and yiaY

  • High cofitness between biosynthetic genes and dependent enzymes confirms functional relationships

  • Differences in cofitness patterns may suggest specialized roles or conditional importance

• Validation Approaches:

  • Construct individual and combinatorial gene deletions to verify cofitness predictions

  • Test growth on specific carbon sources requiring PQQ-dependent enzymes

  • Measure PQQ production in various mutant backgrounds

What statistical approaches are most appropriate for analyzing growth defects in pqqF mutants?

Statistical Framework for Growth Phenotype Analysis:

• Growth Curve Parameter Extraction:

  • Calculate maximum growth rate (μmax) using log-linear regression during exponential phase

  • Determine lag phase duration using time to reach OD threshold

  • Calculate final biomass yield (maximum OD)

  • Fit data to Gompertz or logistic growth models for comprehensive parameter extraction

• Statistical Tests for Comparing Strains:

  • Student's t-test or ANOVA with post-hoc tests (Tukey's HSD) for comparing means between few conditions

  • For comparing multiple conditions and genetic backgrounds simultaneously, use two-way ANOVA with interaction terms

  • For non-normally distributed data, apply non-parametric tests like Mann-Whitney U or Kruskal-Wallis

• Experimental Design Considerations:

  • Include biological replicates (n≥3) and technical replicates

  • Include appropriate controls:

    • Wild-type strain

    • Complemented mutant strain

    • PQQ-supplemented mutant

  • Test multiple carbon sources to profile phenotype specificity

• Advanced Analysis Methods:

  • Principal Component Analysis (PCA) to identify patterns across multiple growth parameters

  • Hierarchical clustering to group similar phenotypes

  • Growth curve fitting to mechanistic models incorporating enzyme kinetics

• Presentation of Results:

  • Growth curves with error bars (standard deviation or standard error)

  • Bar graphs for specific parameters with statistical significance indicators

  • Heat maps for comprehensive phenotype visualization across multiple conditions

Example Analysis from Research:
In P. fluorescens, pqqF mutants exhibited an inability to utilize ethanol as a carbon source and showed enhanced production of pyoluteorin (Plt) . These phenotypes were reversed by complementation with pqq+ recombinant plasmids or by the addition of 16 nM PQQ, confirming that the growth defect was specifically due to PQQ deficiency . Similar statistical approaches can be applied to P. putida pqqF mutants to quantify growth defects on various carbon sources and the rescue effect of genetic complementation or PQQ supplementation.

How can researchers address inconsistent PQQ production levels in recombinant systems?

Systematic Troubleshooting Approach:

• Media Composition Analysis:

  • Trace element availability significantly impacts PQQ biosynthesis

  • Compare different minimal media formulations (M9 vs. MP) with defined trace element concentrations

  • Systematically vary concentrations of key metals (Cu, Fe, Mn, Zn) to identify optimal conditions

  • Consider chelating agents that may sequester essential metals

• Expression System Optimization:

  • Verify all PQQ biosynthesis genes (pqqA-G) are present and properly expressed

  • Check codon optimization for heterologous expression systems

  • Ensure balanced expression of all pathway components through promoter engineering

  • Consider genomic integration versus plasmid-based expression for stability

• Process Parameter Control:

  • Maintain precise pH control (optimal range typically pH 6.5-7.5)

  • Control dissolved oxygen levels (PQQ biosynthesis is oxygen-dependent)

  • Optimize temperature for enzyme activity versus stability

  • Implement fed-batch strategies to avoid nutrient limitation

• Analytical Method Validation:

  • Verify extraction efficiency with PQQ spike recovery tests

  • Run appropriate standards with each analytical batch

  • Consider matrix effects in complex media

  • Use multiple detection methods to cross-validate results

• Genetic Stability Monitoring:

  • Check for mutations in PQQ biosynthesis genes after prolonged cultivation

  • Verify plasmid stability in the absence of selection pressure

  • Monitor expression levels of key biosynthetic enzymes over time

Case-Specific Solutions:
If inconsistent PQQ production is observed, researchers should systematically test the following potential solutions:

• Supplement medium with trace metals, particularly copper and iron

• Adjust medium pH to optimize enzyme activity

• Verify genetic integrity of the pqqF gene and other biosynthesis genes

• Consider co-expression of chaperones to improve protein folding

• Implement adaptive laboratory evolution to select for improved PQQ producers

What are the potential pitfalls in designing genetic constructs for pqqF expression and analysis?

Common Pitfalls and Mitigation Strategies:

• Promoter Selection Issues:

  • Pitfall: Inappropriate promoter strength leading to toxicity (too strong) or insufficient expression (too weak)

  • Solution: Test a range of promoters with different strengths (constitutive and inducible)

  • Method: Construct a promoter library and measure both PqqF expression and PQQ production

• Codon Usage Problems:

  • Pitfall: Non-optimized codons causing translation inefficiency in heterologous hosts

  • Solution: Perform codon optimization for the expression host while preserving important regulatory sequences

  • Method: Use codon optimization algorithms that account for tRNA abundance in the target organism

• Fusion Tag Interference:

  • Pitfall: Affinity tags disrupting protein folding or activity

  • Solution: Test multiple tag positions (N-terminal, C-terminal) or use cleavable tags

  • Method: Compare activity of tagged versus untagged protein after purification

• Operon Structure Disruption:

  • Pitfall: Modifying pqqF without considering polar effects on downstream genes

  • Solution: Design constructs that maintain native operon structure or provide complementation of all affected genes

  • Method: Use RT-PCR to verify expression of downstream genes in the engineered strain

• Regulatory Element Oversight:

  • Pitfall: Excluding important regulatory sequences when cloning pqqF

  • Solution: Include adequate upstream and downstream regions in cloning strategy

  • Method: Perform 5' RACE to identify transcription start sites and ensure all regulatory sequences are included

• Incompatibility with Host Factors:

  • Pitfall: Recombinant PqqF may require specific host factors absent in heterologous systems

  • Solution: Co-express potential interacting partners (e.g., PqqG) or use closely related hosts

  • Method: Perform pull-down assays to identify interacting proteins in the native host

Example Design Considerations:
When designing a pqqF expression construct, researchers should consider:

• Include at least 200-300 bp upstream of the start codon to capture potential regulatory elements

• If using an inducible system, ensure tight regulation to prevent toxicity

• Consider the natural operon structure and potential co-expression requirements

• Include appropriate ribosome binding sites optimized for the host

• Verify construct design by sequencing before transformation

How might synthetic biology approaches be applied to engineer enhanced PQQ production systems?

Synthetic Biology Strategies for PQQ Production Enhancement:

• Pathway Optimization:

  • Modular assembly of PQQ biosynthesis genes with optimized expression levels

  • Balancing gene expression using combinations of promoters, RBSs, and terminators of varying strengths

  • Dynamic pathway regulation using biosensors responsive to pathway intermediates or PQQ itself

  • Methodological approach: Employ automated design-build-test-learn cycles with multivariate optimization

• Chassis Engineering:

  • Genome reduction to eliminate competing pathways and improve metabolic efficiency

  • Modification of central carbon metabolism to increase precursor supply (glutamate and tyrosine)

  • Engineering of membrane transporters to improve PQQ export or reduce toxicity

  • Approach: Use systems biology models to identify and eliminate non-essential genes while preserving robust growth

• Protein Engineering:

  • Structure-guided design of PqqF variants with improved catalytic efficiency

  • Engineering of substrate specificity to accept modified precursor peptides

  • Stability engineering to enhance thermostability or pH tolerance

  • Method: Combine computational design with high-throughput screening of mutant libraries

• Innovative Production Formats:

  • Cell-free systems utilizing purified PQQ biosynthetic enzymes

  • Immobilized whole-cell catalysts with enhanced stability

  • Continuous production systems with in situ product recovery

  • Approach: Design bioreactor configurations that maximize volumetric productivity while minimizing inhibition

• Cross-Species Optimization:

  • Mining biodiversity for superior PQQ biosynthesis genes

  • Creating synthetic hybrid pathways combining efficient components from different organisms

  • Horizontal transfer of optimized pathways into industrial production hosts

  • Method: Comparative genomics combined with functional screening of environmental isolates

Expected Outcomes and Metrics:

• 5-10 fold increase in specific PQQ production rate

• Reduced byproduct formation

• Improved production stability over extended cultivation periods

• Simplified downstream processing due to enhanced purity

How can researchers leverage systems biology to understand the broader metabolic impact of pqqF and PQQ biosynthesis?

Systems Biology Framework for PQQ Research:

• Multi-omics Integration Approach:

  • Transcriptomics: Compare wild-type and ΔpqqF strains to identify differentially expressed genes

  • Proteomics: Quantify protein abundance changes, particularly in carbon metabolism pathways

  • Metabolomics: Profile intracellular and extracellular metabolites to identify bottlenecks and overflow metabolism

  • Fluxomics: Use 13C metabolic flux analysis to quantify changes in central carbon metabolism

  • Method: Integrate multi-omics data using computational tools like correlation networks or multivariate statistical analyses

• Genome-Scale Metabolic Modeling:

  • Develop or refine existing P. putida genome-scale models to incorporate PQQ biosynthesis and PQQ-dependent reactions

  • Perform flux balance analysis (FBA) to predict growth phenotypes of pqqF mutants

  • Use minimization of metabolic adjustment (MOMA) to predict metabolic adaptations

  • Method: Systematically validate model predictions with experimental growth data on various carbon sources

• Regulatory Network Analysis:

  • Map transcriptional regulators affected by PQQ availability

  • Identify potential feedback mechanisms regulating PQQ biosynthesis

  • Characterize cross-talk between PQQ-dependent pathways and central metabolism

  • Approach: Combine ChIP-seq data with transcriptomics to build regulatory network models

• Interactome Mapping:

  • Identify protein-protein interactions involving PqqF using techniques like bacterial two-hybrid screening

  • Map the complete protein interaction network of the PQQ biosynthesis pathway

  • Identify potential regulatory interactions with metabolic enzymes

  • Method: Use affinity purification coupled with mass spectrometry to identify interaction partners

• Community-Level Analysis:

  • Investigate the role of PQQ as a potential public good in microbial communities

  • Examine cross-feeding interactions based on PQQ exchange

  • Study competition effects between PQQ producers and non-producers

  • Approach: Design synthetic microbial consortia with labeled strains to track population dynamics

Research Application Example:
The relationship between PqqF, glucose metabolism, and secondary metabolite production could be explored through a systems biology approach. In P. fluorescens, PqqF mutations lead to enhanced production of pyoluteorin , while in P. putida, the relationship between PQQ-dependent glucose dehydrogenase (Gcd) and carbon utilization has been established . An integrated systems approach would allow researchers to understand how these phenotypes are connected through global metabolic and regulatory networks.

What are the most significant unanswered questions regarding PqqF function in PQQ biosynthesis?

Several critical knowledge gaps remain in our understanding of PqqF function and PQQ biosynthesis:

• Structural Basis of Substrate Recognition: Despite functional characterization, the three-dimensional structure of P. putida PqqF remains unsolved. Determining this structure would reveal the molecular basis of substrate recognition and catalysis, potentially enabling rational engineering of enhanced variants.

• Regulatory Mechanisms: The transcriptional and post-translational regulation of pqqF expression remains poorly understood. Identifying the environmental signals and regulatory proteins that control PqqF production would provide insights into the physiological contexts for PQQ biosynthesis.

• Protein-Protein Interactions: While evidence suggests PqqF may interact with PqqG in some organisms , the complete set of protein-protein interactions involving PqqF in P. putida remains uncharacterized. Mapping these interactions would clarify the organization of the biosynthetic machinery.

• Catalytic Mechanism: The precise catalytic mechanism of PqqF, including the roles of specific amino acid residues and potential cofactors, requires further investigation to fully understand this crucial step in PQQ biosynthesis.

• Evolution and Diversity: Comparative genomic analysis across diverse bacteria could reveal how PqqF has evolved and adapted to different hosts and metabolic contexts, potentially identifying more efficient natural variants.

Addressing these questions will require interdisciplinary approaches combining structural biology, biochemistry, genetics, and systems biology to fully elucidate the role of PqqF in PQQ biosynthesis and bacterial metabolism.

How might understanding PqqF contribute to broader applications in biotechnology and synthetic biology?

Understanding PqqF function has significant implications for various applications:

• Biocatalysis and Green Chemistry: Enhanced production of PQQ through PqqF engineering could support the development of PQQ-dependent enzymatic systems for industrial biocatalysis, particularly for alcohol and aldehyde oxidation reactions under mild conditions.

• Biosensor Development: PQQ-dependent dehydrogenases are valuable components in biosensors for glucose, alcohols, and aldehydes. Optimized PQQ production through PqqF engineering could improve biosensor sensitivity and stability.

• Metabolic Engineering: Understanding how PqqF and PQQ biosynthesis integrate with central metabolism could inform strategies for engineering P. putida and related organisms for bioproduction of valuable chemicals from renewable feedstocks.

• Agricultural Applications: PQQ-producing bacteria can enhance plant growth through mechanisms involving phosphate solubilization and plant hormone production. Engineering improved PQQ production could enhance biofertilizer capabilities.

• Synthetic Biology Toolbox Expansion: The PQQ biosynthesis pathway represents a modular system that could be incorporated into synthetic biology designs for creating new metabolic capabilities or cellular functions.