Recombinant Pseudomonas putida Sulfoxide reductase catalytic subunit YedY (yedY)

What is Pseudomonas putida Sulfoxide reductase catalytic subunit YedY?

Pseudomonas putida Sulfoxide reductase catalytic subunit YedY is a molybdenum-containing enzyme that catalyzes the reduction of sulfoxide groups in various substrates. This enzyme belongs to the dimethyl sulfoxide (DMSO) reductase family and plays important roles in bacterial sulfur metabolism. In Pseudomonas putida, YedY functions within the cellular redox system, enabling the bacterium to utilize certain sulfur-containing compounds as electron acceptors under specific conditions. The enzyme is particularly significant given P. putida's remarkable metabolic versatility and adaptation to various environmental niches, including soils and sediments containing high levels of heavy metals and organic contaminants .

What role does YedY play in P. putida's metabolic versatility?

YedY contributes to P. putida's remarkable metabolic versatility by expanding the range of sulfur-containing compounds the bacterium can process. P. putida strains are well-known for their ability to break down a wide range of natural and artificial chemicals . The YedY enzyme likely plays a role in this metabolic flexibility by enabling the reduction of sulfoxide groups in various environmental compounds. This capability may be particularly important when P. putida functions as a rhizospheric or endophytic bacterium in plant systems, where it can encounter diverse organic compounds. The enzyme may also contribute to P. putida's survival during stationary phase or nutrient limitation, as sulfoxide reduction can provide alternative electron acceptors during metabolic stress.

What are the optimal conditions for expressing recombinant P. putida YedY in laboratory settings?

For optimal expression of recombinant P. putida YedY, researchers should consider the following methodological approach:

• Expression System Selection: While E. coli is often used as a heterologous host, expressing P. putida proteins in P. putida itself often yields better results due to compatible codon usage and proper folding machinery.

• Growth Conditions:

  • Temperature: 28-30°C (lower than standard E. coli conditions)

  • Media: Defined minimal media supplemented with appropriate carbon sources

  • Induction: IPTG concentration of 0.1-0.5 mM for lac-based promoters

• Cofactor Supplementation: Addition of molybdate (1-10 μM Na₂MoO₄) to the growth media ensures proper incorporation of the molybdenum cofactor essential for YedY activity.

• Oxygen Conditions: Semi-aerobic conditions often improve expression of redox enzymes like YedY.

The experimental design should include careful monitoring of growth phases, as expression during the early stationary phase has shown improved yields for similar enzymes. P. putida populations typically reach maximum cell density (approximately 7.7 × 10⁹ cells/mL) after 24 hours of growth before entering death phase, suggesting optimal induction timing shortly before this point .

How should researchers design experiments to study YedY adaptation during long-term stationary phase?

To effectively study YedY adaptation during long-term stationary phase (LTSP), researchers should implement a systematic experimental design approach:

• Experimental Timeline:

  • Establish cultures and monitor growth into stationary phase

  • Begin LTSP measurements at approximately day 10, when P. putida typically enters true LTSP

  • Continue observations for at least 127 days to capture long-term adaptations

• Sampling Strategy:

  • Collect samples at regular intervals (e.g., days 10, 24, 60, 90, and 127)

  • Maintain parallel populations (minimum of three) to account for stochastic evolutionary events

• Analytical Methods:

  • Whole-genome sequencing of multiple clones at each timepoint to identify mutations

  • RT-qPCR to measure yedY expression levels throughout LTSP

  • Enzyme activity assays to correlate genetic changes with functional outcomes

• Controls:

  • Include ancestral strains as reference points

  • Maintain non-evolving frozen stocks from each timepoint

This experimental approach allows researchers to detect both population-level adaptations and the emergence of specific lineages with mutations affecting YedY function. As observed in LTSP studies with P. putida, mutations accumulate over time and often occur in a convergent manner across independent populations , making it essential to sequence multiple clones per timepoint.

What controls should be included when measuring YedY enzymatic activity?

A comprehensive set of controls is essential for accurate measurement of YedY enzymatic activity:

Additionally, researchers should implement time-course measurements to ensure linearity of the reaction and multiple substrate concentrations to determine kinetic parameters accurately. When working with P. putida strains known for their diverse metabolic capabilities , it's particularly important to validate the specificity of the assay for YedY activity versus other potential reductases present in the organism.

How do mutations in DNA repair systems affect YedY evolution during prolonged stationary phase?

Mutations in DNA repair systems, particularly in mismatch repair genes like mutL, significantly impact YedY evolution during prolonged stationary phase. Research with P. putida has shown that mutator phenotypes emerge during long-term stationary phase (LTSP) experiments . These mutator strains accumulate mutations at a much higher rate than non-mutator clones, potentially accelerating YedY adaptation.

The impact manifests in several ways:

• Mutation Rate Amplification: Mutator clones with defective mismatch repair accumulate significantly more mutations affecting yedY and other genes. In P. putida LTSP experiments, such mutators emerged and persisted throughout the 4-month experimental period .

• Lineage Persistence: Despite higher mutation loads, mutator lineages containing yedY mutations can persist throughout LTSP, suggesting these mutations may confer selective advantages.

• Convergent Evolution: Similar yedY mutations appear across independent populations, indicating selective pressure on specific functional domains of the protein .

For researchers studying YedY evolution, it's crucial to screen for mutator phenotypes in evolved populations, as their presence significantly alters evolutionary trajectories. The proportion of mutations affecting YedY function versus structure should be analyzed to distinguish between adaptive changes and hitchhiking mutations accumulating due to the mutator phenotype.

What methodologies can effectively distinguish between adaptive and neutral mutations in the yedY gene?

Distinguishing between adaptive and neutral mutations in the yedY gene requires a multi-faceted methodological approach:

• Frequency-Based Analysis:

  • Track mutation frequencies across multiple timepoints

  • Adaptive mutations typically increase in frequency over time

  • Implement statistical tests to identify significant frequency changes

• Convergent Evolution Assessment:

  • Compare mutations across independent populations

  • Mutations occurring in similar positions across multiple populations suggest adaptive value

  • Use statistical frameworks like dN/dS ratios to identify selection signatures

• Functional Impact Prediction and Validation:

  • Computational prediction of mutation effects on protein structure/function

  • Site-directed mutagenesis to introduce specific mutations

  • Enzymatic assays comparing wild-type and mutant YedY activity

• Competition Experiments:

  • Direct competition between ancestral and evolved strains

  • Fitness measurements under relevant environmental conditions

  • Allelic replacement to isolate effects of specific yedY mutations

• Structural Mapping:

  • Map mutations onto protein structure models

  • Cluster analysis to identify hotspots in functional domains

  • Correlate with known catalytic or binding sites

This comprehensive approach allows researchers to move beyond merely documenting mutations to understanding their adaptive significance in P. putida's evolutionary trajectory during prolonged stationary phase .

How can single-cell approaches enhance our understanding of YedY heterogeneity within P. putida populations?

Single-cell approaches provide powerful insights into YedY heterogeneity within P. putida populations, revealing dynamics that population-level analyses might miss:

• Single-Cell RNA Sequencing (scRNA-seq):

  • Reveals transcriptional heterogeneity of yedY expression

  • Identifies subpopulations with distinct expression profiles

  • Allows correlation with other metabolic pathways at single-cell resolution

• Time-Lapse Microscopy with Fluorescent Reporters:

  • Monitor YedY-GFP fusion protein localization and expression dynamics

  • Track lineages through multiple generations

  • Correlate expression with cellular phenotypes (growth rate, morphology)

• Flow Cytometry and Cell Sorting:

  • Quantify YedY heterogeneity across thousands of cells

  • Sort subpopulations for downstream analysis

  • Monitor population shifts during adaptation

• Single-Cell Genome Sequencing:

  • Identify genetic variants within individual cells

  • Link genotype to phenotype at single-cell level

  • Reconstruct lineage relationships within mixed populations

These methodologies are particularly valuable for studying P. putida populations during LTSP, where independently evolving lineages are established early and persist throughout experiments . Single-cell approaches can reveal how YedY diversity contributes to population resilience, especially in fluctuating environments where P. putida's metabolic versatility provides adaptive advantages.

How does P. putida YedY function compare with homologs in other Pseudomonas species?

The YedY sulfoxide reductase in P. putida exhibits both conserved and divergent features compared to homologs in other Pseudomonas species:

• Conserved Catalytic Mechanism:

  • The molybdenum cofactor binding motif is highly conserved across Pseudomonas species

  • The core catalytic mechanism of sulfoxide reduction follows similar electron transfer pathways

• Substrate Specificity Variations:

  • P. putida YedY likely shows broader substrate specificity compared to some other Pseudomonas species, reflecting its remarkable metabolic versatility

  • P. aeruginosa YedY may have evolved more specialized substrate preferences related to its pathogenic lifestyle

  • Environmental Pseudomonas species show adaptations to their specific ecological niches

• Regulatory Differences:

  • Expression control mechanisms vary significantly between species

  • P. putida YedY regulation is likely integrated with its diverse metabolic pathways

  • Stress response activation differs based on species-specific environmental adaptations

• Structural Variations:

  • While core domains remain conserved, substrate-binding regions show species-specific adaptations

  • These structural differences reflect the distinct evolutionary pressures facing each Pseudomonas species

These comparative insights highlight how YedY has evolved differently across the Pseudomonas genus, with P. putida's version reflecting its adaptation to diverse environments and metabolic capabilities .

What insights can be gained by comparing E. coli and P. putida YedY mutation patterns during long-term evolution?

Comparative analysis of E. coli and P. putida YedY mutation patterns during long-term evolution reveals important insights into both convergent and divergent adaptive strategies:

This comparative approach highlights how fundamental evolutionary processes operate similarly across bacterial species while producing species-specific adaptive outcomes, reflecting their unique ecological and metabolic characteristics .

How do post-translational modifications of YedY differ between P. putida and E. coli?

Post-translational modifications (PTMs) of YedY show notable differences between P. putida and E. coli, reflecting their distinct cellular environments and evolutionary histories:

These differences in PTMs contribute significantly to the functional divergence between P. putida and E. coli YedY enzymes. The P. putida enzyme's modifications likely reflect adaptations to its diverse metabolic capabilities and environmental niches , while E. coli YedY modifications are optimized for its more specialized metabolism. Researchers studying recombinant P. putida YedY should consider these PTM differences when designing expression systems and purification strategies, as heterologous expression might not reproduce all native modifications.

How should researchers address discrepancies in YedY activity measurements between different experimental setups?

Addressing discrepancies in YedY activity measurements between experimental setups requires a systematic troubleshooting approach:

• Standardize Experimental Conditions:

  • Buffer composition (pH, ionic strength)

  • Temperature control (±0.5°C precision)

  • Substrate purity and concentration

  • Enzyme preparation methods

• Implement Cross-Validation Strategies:

  • Use multiple detection methods for enzyme activity

  • Compare direct and coupled assay results

  • Validate with independent enzyme preparations

• Statistical Analysis Framework:

  • Apply appropriate statistical tests for comparing methods

  • Utilize Bland-Altman plots to assess agreement between methods

  • Calculate coefficient of variation between replicates and methods

• Control for P. putida-Specific Factors:

  • Account for potential interfering metabolites

  • Consider the impact of P. putida's diverse redox systems

  • Test for inhibitory compounds in cell lysates

• Data Normalization Protocol:

  • Standardize against well-characterized reference enzymes

  • Develop conversion factors between methods

  • Report specific activity in multiple formats to facilitate comparison

When designing experimental protocols, researchers should carefully consider how P. putida's unique physiological characteristics might influence YedY activity measurements compared to model organisms like E. coli . This includes differences in cellular redox balance, potential interfering metabolites, and co-purifying proteins that might affect activity determinations.

What statistical approaches are most appropriate for analyzing YedY adaptation across evolutionary timescales?

The analysis of YedY adaptation across evolutionary timescales requires sophisticated statistical approaches tailored to evolutionary data:

• Time-Series Analysis Methods:

  • Autoregressive integrated moving average (ARIMA) models

  • Wavelet analysis for identifying periodic patterns

  • Change-point detection algorithms to identify evolutionary transitions

• Phylogenetic Comparative Methods:

  • Bayesian evolutionary analysis for reconstructing ancestral states

  • Phylogenetic generalized least squares for controlling phylogenetic non-independence

  • Tests for convergent evolution across independent lineages

• Population Genetics Frameworks:

  • Wright-Fisher population models with selection

  • Calculation of selection coefficients across timepoints

  • Tests for selective sweeps affecting yedY and linked genes

• Machine Learning Applications:

  • Supervised learning to classify adaptive vs. neutral mutations

  • Unsupervised clustering to identify mutation patterns

  • Deep learning for predicting mutation effects on YedY function

• Experimental Design Considerations:

  • Power analysis to determine appropriate sampling depth

  • Randomization procedures to minimize batch effects

  • Factorial design to test interactions between variables

These statistical approaches should be applied with careful consideration of P. putida's population dynamics during LTSP, including the emergence of mutator phenotypes and independently evolving lineages that establish early and persist throughout experiments .

How can researchers effectively distinguish between YedY activity changes due to genetic adaptation versus phenotypic plasticity?

Distinguishing between genetic adaptation and phenotypic plasticity in YedY activity requires a multifaceted experimental approach:

• Reciprocal Transplant Experiments:

  • Expose ancestral and evolved strains to both ancestral and novel conditions

  • Measure YedY activity across all strain-by-environment combinations

  • Genetic adaptation produces consistent differences regardless of environment

  • Plasticity produces environment-dependent responses

• Genetic Manipulation Tests:

  • Introduce evolved yedY alleles into ancestral genetic backgrounds

  • Replace evolved yedY with ancestral version in evolved strains

  • Quantify how much phenotypic change is explained by yedY mutations alone

• Transcriptional Response Analysis:

  • Compare yedY expression patterns between ancestral and evolved strains

  • Distinguish between mutations affecting the gene itself versus its regulation

  • Map regulatory networks controlling yedY expression

• Epigenetic Characterization:

  • Assess DNA methylation patterns around the yedY locus

  • Measure persistence of activity changes after growth in non-selective conditions

  • Quantify heritability of non-genetic components

• Environmental Switching Experiments:

  • Measure adaptation speed when conditions change

  • Track YedY activity through multiple environmental transitions

  • Quantify the stability of adapted states

This experimental framework allows researchers to parse the relative contributions of genetic adaptation versus phenotypic plasticity to YedY functionality in P. putida populations. This distinction is particularly important given P. putida's remarkable metabolic versatility and environmental adaptability , which may involve both genetic and non-genetic mechanisms.