Recombinant Desulfovibrio vulgaris Maf-like protein DVU_0527 (DVU_0527)

Basic Research Questions

• What is Desulfovibrio vulgaris Maf-like protein DVU_0527?

  DVU_0527 is a nucleoside triphosphate pyrophosphatase that belongs to the Maf (multicopy associated filamentation) family, specifically the YhdE subfamily. It is a 210 amino acid protein with a molecular mass of approximately 22.224 kDa found in Desulfovibrio vulgaris strain Hildenborough (ATCC 29579/DSM 644/NCIMB 8303) . Maf proteins represent a conserved family implicated in cell division arrest, though their precise biochemical functions have only recently been elucidated. DVU_0527 specifically functions as a dTTP/UTP pyrophosphatase that hydrolyzes canonical nucleotides and may also play a role in preventing the incorporation of modified nucleotides into cellular nucleic acids .

• What is the biochemical activity of DVU_0527 and how does it compare to other Maf proteins?

  DVU_0527 exhibits nucleotide pyrophosphatase activity, primarily hydrolyzing dTTP and UTP. This activity is consistent with other characterized Maf proteins which have been shown to hydrolyze canonical nucleotides (dTTP, UTP, and CTP) as well as modified nucleotides such as 5-methyl-UTP, pseudo-UTP, 5-methyl-CTP, and 7-methyl-GTP .

  The mechanism of action involves:

  • Recognition of specific nucleotide substrates

  • Hydrolysis of the pyrophosphate bond

  • Release of pyrophosphate and monophosphate products

  DVU_0527, as part of the YhdE subfamily, likely has substrate preferences that differ somewhat from members of the YceF subfamily of Maf proteins, though both subfamilies exhibit nucleotide pyrophosphatase activity . Experimental approaches to characterize this activity typically include in vitro enzymatic assays with purified recombinant protein and various nucleotide substrates, followed by HPLC analysis of reaction products.

• How is DVU_0527 structurally related to other nucleotide-hydrolyzing enzymes?

  DVU_0527 shares structural features with other nucleotide-hydrolyzing enzymes, particularly those in the Maf family and ITPases (inosine triphosphate pyrophosphatases). Key structural elements include:

  • A nucleotide binding pocket that accommodates the base, sugar, and phosphate components

  • Specific residues involved in base recognition, which determine substrate specificity

  • Catalytic residues necessary for the hydrolysis reaction

  Structural analysis of Maf proteins has revealed a pocket equivalent to the base recognition site of ITPases, suggesting that Maf proteins bind and hydrolyze nucleotide substrates in a similar manner . This structural homology provides insights into the evolution of nucleotide-hydrolyzing enzymes and their specialized functions across different organisms.

Advanced Research Methodology

• What are the optimal methods for expressing and purifying recombinant DVU_0527?

  Based on successful approaches with similar proteins, the following methodology is recommended:

  Expression System:

  • Host: E. coli BL21(DE3) or similar expression strain

  • Vector: pET-based vector with N-terminal His-tag or MBP fusion for improved solubility

  • Induction: 0.1-0.5 mM IPTG at OD600 ~0.6-0.8

  • Growth temperature: 16-18°C post-induction (to minimize inclusion body formation)

  Purification Protocol:

  1. Cell lysis using sonication or French press in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM DTT, and protease inhibitors

  2. Clarification by centrifugation (20,000 × g, 30 min, 4°C)

  3. Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin

  4. Size exclusion chromatography to obtain homogeneous protein

  Protein Quality Assessment:

  • SDS-PAGE analysis (expected size ~22.2 kDa plus tag)

  • Western blot confirmation

  • Activity assay using dTTP/UTP as substrates

  If protein solubility is problematic, consider the refolding approach used for recombinant rubrerythrin from D. vulgaris: solubilizing inclusion bodies in 3 M guanidinium chloride followed by gradual dilution of the denaturant .

• How can substrate specificity of DVU_0527 be comprehensively analyzed?

  A thorough analysis of DVU_0527 substrate specificity requires multiple complementary approaches:

  In Vitro Enzymatic Assays:

  1. Prepare reaction mixtures containing purified DVU_0527 (1-5 μM) and various nucleotide substrates (100-500 μM)

  2. Include potential substrates:

     • Canonical nucleotides: dTTP, UTP, CTP, ATP, GTP

     • Modified nucleotides: 5-methyl-UTP, pseudo-UTP, 5-methyl-CTP, 7-methyl-GTP

  3. Measure activity using:

     • Malachite green assay for released phosphate

     • HPLC analysis of reaction products

     • Coupled enzymatic assays

  Kinetic Analysis:

  1. Determine Km and kcat values for each substrate

  2. Calculate catalytic efficiency (kcat/Km) to quantify preference

  Structural Studies:

  1. Co-crystallization with substrate analogs or products

  2. Molecular docking simulations

  Comparative Analysis:
  Create a substrate specificity profile comparing DVU_0527 with other Maf proteins to identify subfamily-specific patterns.

  Data should be presented in a comprehensive table:

  Substrate	Km (μM)	kcat (s⁻¹)	kcat/Km (M⁻¹s⁻¹)	Relative Activity (%)
  dTTP
  UTP
  5-methyl-UTP
  etc.

• What experimental design would effectively elucidate DVU_0527's role in cell division?

  To investigate DVU_0527's role in cell division, a multi-faceted experimental approach is required:

  Genetic Manipulation:

  1. Generate a DVU_0527 knockout mutant in D. vulgaris using marker replacement mutagenesis

  2. Create a complementation strain expressing DVU_0527 from a plasmid

  3. Develop a strain with inducible overexpression of DVU_0527

  Phenotypic Characterization:

  1. Growth curve analysis under various conditions

  2. Microscopic examination of cell morphology:

     • Phase contrast microscopy for filamentation

     • Fluorescence microscopy with DNA and membrane staining

  3. Flow cytometry to analyze DNA content and cell size distribution

  Molecular Interactions:

  1. Pull-down assays to identify protein interaction partners

  2. Bacterial two-hybrid screening

  3. Co-immunoprecipitation with known cell division proteins

  Localization Studies:

  1. Fluorescent protein tagging of DVU_0527

  2. Time-lapse microscopy during cell division

  Transcriptional Profiling:

  1. RNA-seq analysis comparing wild-type and mutant strains

  2. qRT-PCR validation of differentially expressed cell division genes

  This experimental design follows the principles outlined in the experimental research approach , including defining variables, forming hypotheses, establishing treatments, and measuring dependent variables with appropriate controls.

• How can transcriptomics and proteomics be integrated to study DVU_0527 regulation?

  An integrated omics approach provides comprehensive insights into DVU_0527 regulation:

  Experimental Setup:

  1. Growth conditions:

     • Standard conditions (lactate/sulfate medium)

     • Stress conditions (NaCl, nitrite, chromate, oxygen exposure)

     • Growth phase variations (exponential vs. stationary)

  2. Sample collection for parallel analysis:

     • RNA extraction for transcriptomics

     • Protein extraction for proteomics

     • Metabolite extraction for metabolomics

  Transcriptomics Analysis:

  1. RNA-seq to quantify DVU_0527 mRNA levels

  2. Identification of co-regulated genes

  3. Promoter analysis for potential regulatory elements

  4. Comparative analysis with known regulons (e.g., Crp/Fnr-type regulators )

  Proteomics Analysis:

  1. Targeted MS/MS for DVU_0527 protein quantification

  2. Global proteome analysis to identify correlations

  3. Post-translational modifications assessment

  Data Integration:

  1. Multi-omics data visualization

  2. Pathway enrichment analysis

  3. Network reconstruction of regulatory interactions

  4. Correlation analysis between mRNA and protein levels

  Validation:

  1. qRT-PCR for key transcripts

  2. Western blot for protein levels

  3. ChIP-seq to identify transcription factor binding sites

  This integrative approach has been successfully applied to study gene expression in D. vulgaris under various conditions and can be specifically tailored to understand DVU_0527 regulation.

• What methods are effective for investigating DVU_0527's role in preventing incorporation of modified nucleotides?

  To study DVU_0527's function in preventing incorporation of modified nucleotides:

  In Vivo Approaches:

  1. Generate DVU_0527 knockout and overexpression strains

  2. Expose cells to modified nucleosides or nucleotide precursors

  3. Extract genomic DNA and assess incorporation of modifications via:

     • Mass spectrometry analysis of nucleoside composition

     • Sequencing methods that detect modified bases (e.g., BS-seq, oxBS-seq)

  4. Measure mutation rates using reporter systems

  In Vitro Analysis:

  1. Purify recombinant DVU_0527

  2. Assess hydrolysis activity against modified nucleotides:

     • 5-methyl-UTP

     • Pseudo-UTP

     • 5-methyl-CTP

     • 7-methyl-GTP

  3. Compare kinetic parameters with those of canonical nucleotides

  Cell-Free Systems:

  1. Develop a transcription/translation system with/without DVU_0527

  2. Supply modified nucleotides and measure their incorporation

  3. Analyze resulting RNA/protein products for errors

  Structural Studies:

  1. Co-crystallize DVU_0527 with modified nucleotides

  2. Determine binding affinity using isothermal titration calorimetry

  3. Identify key residues involved in substrate recognition

  The relationship between findings from in vitro biochemical data and in vivo phenotypes should be carefully evaluated, as demonstrated in studies of other nucleotide-processing enzymes .

Experimental Techniques

• What mutagenesis approaches can reveal DVU_0527's catalytic mechanism?

  Systematic mutagenesis can uncover the catalytic mechanism of DVU_0527:

  Site-Directed Mutagenesis Strategy:

  1. Catalytic Site Residues:

     • Identify conserved residues in alignment of Maf family proteins

     • Target acidic residues (Asp, Glu) potentially involved in metal coordination

     • Mutate potential nucleophilic residues to alanine

  2. Substrate Specificity Determinants:

     • Identify residues in the base recognition pocket

     • Create mutants that may alter specificity between dTTP and UTP

     • Generate chimeric proteins with regions from other Maf family members

  3. Structure-Guided Approach:

     • Use homology models based on related Maf structures

     • Target residues predicted to interact with substrates

     • Design mutations that might enhance or alter activity

  Mutant Analysis Protocol:

  1. Expression and Purification:

     • Express wild-type and mutant proteins under identical conditions

     • Verify proper folding using circular dichroism spectroscopy

  2. Activity Assays:

     • Measure kinetic parameters (Km, kcat) for each mutant

     • Compare substrate preferences and catalytic efficiencies

     • Determine pH and metal ion dependencies

  3. Structural Validation:

     • Obtain crystal structures of key mutants when possible

     • Use thermal shift assays to assess stability changes

  Data Analysis and Interpretation:

  1. Create a comprehensive table of mutational effects:

     Mutation	Activity (% WT)	Km Change	kcat Change	Structural Effect
     D45A
     H78A
     etc.

  2. Map mutations onto structural models to visualize patterns

  3. Compare results with other characterized Maf proteins

  This systematic approach will help define the catalytic mechanism and provide insights for potential future applications or inhibitor design.

• How can advanced structural biology techniques be applied to study DVU_0527?

  Multiple structural biology techniques can provide complementary insights into DVU_0527:

  X-ray Crystallography:

  1. Crystallization Optimization:

     • Screen various buffer conditions, pH ranges, and precipitants

     • Test different protein concentrations (5-15 mg/ml)

     • Use additives and nucleotide analogs to stabilize specific conformations

  2. Data Collection and Processing:

     • Collect high-resolution diffraction data at synchrotron facilities

     • Process data using XDS or MOSFLM software

     • Solve structure by molecular replacement using other Maf structures

  Cryo-Electron Microscopy:

  1. Sample Preparation:

     • Prepare DVU_0527 at 0.5-5 mg/ml on appropriate grids

     • Use both negative staining and vitrification approaches

  2. Data Collection:

     • Collect data on high-end microscopes with direct electron detectors

     • Process using RELION or cryoSPARC software

  Nuclear Magnetic Resonance (NMR):

  1. Sample Preparation:

     • Express isotopically labeled protein (15N, 13C)

     • Optimize buffer conditions for stability

  2. Experiments:

     • Backbone assignment using HSQC, HNCA, HNCACB experiments

     • Study dynamics and substrate binding

  Small-Angle X-ray Scattering (SAXS):

  1. Data Collection:

     • Collect data on DVU_0527 with/without nucleotide substrates

     • Analyze different oligomeric states

  2. Analysis:

     • Generate solution envelopes and compare with crystal structures

     • Study conformational changes upon substrate binding

  Integrative Structural Biology:

  1. Combine data from multiple techniques using integrative modeling platforms

  2. Include biochemical constraints from crosslinking and mutagenesis

  3. Develop comprehensive structural models of DVU_0527 in different states

  These approaches have proven valuable for studying protein complexes in D. vulgaris and would provide detailed insights into DVU_0527's structure-function relationships.

• What functional genomics approaches can identify DVU_0527's role in stress responses?

  Comprehensive functional genomics can reveal DVU_0527's role in stress responses:

  Knockout and Complementation Studies:

  1. Generate DVU_0527 deletion mutant using marker replacement mutagenesis

  2. Create complementation strain with wild-type DVU_0527

  3. Develop strains with point mutations in catalytic residues

  Stress Response Profiling:

  1. Stress Conditions to Test:

     • Oxidative stress (H₂O₂, air exposure)

     • Nitrosative stress (nitrite, nitrate)

     • Salt stress (NaCl)

     • Heavy metal stress (chromate)

     • Nutrient limitation

     • Heat shock

  2. Growth and Fitness Measurements:

     • Monitor growth curves under each stress condition

     • Perform competitive fitness assays with wild-type

     • Measure survival rates following acute stress exposure

  Transcriptional Response Analysis:

  1. RNA-Seq Analysis:

     • Compare transcriptomes of wild-type and ΔdVU_0527 under stress

     • Identify differentially regulated genes and pathways

     • Look for changes in expression of other nucleotide metabolism genes

  2. ChIP-Seq for Regulatory Interactions:

     • Identify transcription factors regulating DVU_0527

     • Determine if DVU_0527 deletion affects binding of key regulators

  Integration with Existing D. vulgaris Data:

  1. Compare results with known stress responses in D. vulgaris

  2. Analyze correlation with Crp/Fnr-type regulator responses

  3. Relate findings to changes in biofilm formation

  Validation of Key Findings:

  1. Targeted gene expression analysis by qRT-PCR

  2. Protein-level validation by Western blotting

  3. Metabolite analysis focusing on nucleotide pools

  This approach builds on established methods for studying stress responses in D. vulgaris and would reveal DVU_0527's specific contributions to stress adaptation.

• How can biochemical assays be optimized to measure DVU_0527 activity accurately?

  Optimizing biochemical assays for DVU_0527 requires careful consideration of multiple factors:

  Assay Development Strategy:

  1. Purified Protein Preparation:

     • Express with affinity tag (His6 or GST)

     • Purify to >95% homogeneity (verified by SDS-PAGE)

     • Verify activity after each purification step

     • Store with stabilizing agents (glycerol, reducing agents)

  2. Activity Assay Formats:

     a. Direct Product Detection:

     • HPLC separation of substrates and products

     • UV detection at 260 nm for nucleotides

     • Calculate conversion rates from peak areas

     b. Coupled Enzyme Assays:

     • Link pyrophosphate release to NADH oxidation

     • Monitor continuously at 340 nm

     • Include controls for coupling enzyme activity

     c. Colorimetric Phosphate Detection:

     • Malachite green assay for released phosphate

     • Measure absorbance at 620-640 nm

     • Generate standard curve with known phosphate concentrations

  3. Assay Optimization Parameters:

     Parameter	Range to Test	Considerations
     pH	6.0-9.0	Buffer system selection
     Temperature	25-45°C	D. vulgaris optimal growth temperature is 37°C
     Metal ions	Mg²⁺, Mn²⁺, Zn²⁺	Test concentrations from 1-10 mM
     Substrate concentration	10-500 μM	Ensure below saturation for kinetic studies
     Enzyme concentration	0.1-5 μM	Maintain linear reaction rates
     Ionic strength	50-300 mM NaCl	May affect substrate binding

  4. Controls and Validation:

     • Heat-inactivated enzyme control

     • Known pyrophosphatase as positive control

     • Substrate-free and enzyme-free controls

     • Linearity verification with respect to time and enzyme concentration

  5. Data Analysis:

     • Non-linear regression for Michaelis-Menten kinetics

     • Calculate Km, Vmax, kcat, and catalytic efficiency

     • Compare values across different conditions and substrates

  This comprehensive approach will provide reliable, reproducible measurements of DVU_0527 activity under various conditions, essential for understanding its biochemical function.

• What computational approaches can predict novel substrates for DVU_0527?

  Computational methods can help identify potential novel substrates for DVU_0527:

  Homology-Based Approaches:

  1. Sequence Analysis:

     • Identify conserved substrate-binding residues across Maf family

     • Compare with characterized family members of known specificity

     • Generate sequence logos of binding sites

  2. Structural Homology Modeling:

     • Build homology models based on related Maf proteins

     • Identify and characterize the substrate-binding pocket

     • Compare pocket shape and electrostatics with known structures

  Virtual Screening and Docking:

  1. Ligand Library Preparation:

     • Compile library of canonical and modified nucleotides

     • Include potential cellular metabolites

     • Generate appropriate 3D conformations

  2. Molecular Docking:

     • Use AutoDock, GOLD, or Glide for docking simulations

     • Score binding poses based on energy functions

     • Rank compounds by predicted binding affinity

  3. Binding Pose Analysis:

     • Analyze key protein-ligand interactions

     • Identify common features of high-scoring compounds

     • Compare with known substrates

  Molecular Dynamics Simulations:

  1. System Setup:

     • Prepare protein-ligand complexes in explicit solvent

     • Apply appropriate force fields (AMBER, CHARMM)

  2. Simulation Analysis:

     • Calculate binding free energies using MM-PBSA or FEP methods

     • Analyze stability of ligand in binding pocket

     • Identify conformational changes upon binding

  Machine Learning Approaches:

  1. Training Data:

     • Compile activity data for Maf proteins against various substrates

     • Extract physicochemical features of known substrates

  2. Model Development:

     • Train regression or classification models

     • Use cross-validation to assess predictive power

     • Apply to candidate substrate library

  Experimental Validation:

  1. Select top computational hits for biochemical testing

  2. Perform initial screening at fixed concentration

  3. Determine kinetic parameters for promising candidates

  This integrated computational approach, combined with experimental validation, can efficiently identify novel DVU_0527 substrates beyond the currently known spectrum.

Additional Information

• What is known about the evolutionary history of Maf proteins like DVU_0527?

  The evolutionary history of Maf proteins reveals their ancient origins and functional diversification:

  Phylogenetic Distribution:

  Maf proteins are widely distributed across prokaryotes and eukaryotes, suggesting an ancient origin. They can be classified into two main subfamilies:

  1. YhdE subfamily (includes DVU_0527)

     • Present in diverse bacterial phyla

     • Found in some eukaryotes (e.g., S. cerevisiae YOR111W)

  2. YceF subfamily

     • More restricted distribution

     • Different substrate preferences

  Genomic Context Analysis:

  1. In some organisms, Maf genes are associated with methyltransferase genes, suggesting functional relationships

  2. The conservation of this genomic context varies across species

  3. Operonic organization provides clues about functional relationships

  Evolutionary Mechanisms:

  1. Sequence analysis suggests that Maf proteins may have evolved from ancient nucleotide-binding proteins

  2. Structural similarities with ITPases indicate potential common ancestry

  3. The acquisition of specific substrate recognition determinants likely drove specialization

  Methodological Approaches for Evolutionary Analysis:

  1. Sequence-Based Phylogeny:

     • Multiple sequence alignment of diverse Maf proteins

     • Maximum likelihood or Bayesian phylogenetic tree construction

     • Bootstrap analysis to assess tree reliability

  2. Structure-Based Analysis:

     • Comparison of protein folds across nucleotide-hydrolyzing enzymes

     • Identification of conserved structural elements

  3. Genomic Context Analysis:

     • Examination of gene neighborhoods across diverse genomes

     • Detection of co-evolutionary patterns with functionally related genes

  Understanding the evolutionary history of Maf proteins provides context for interpreting the specific functions of DVU_0527 in D. vulgaris and its potential roles in cellular processes.