Recombinant Salmonella dublin Fumarate reductase subunit D (frdD)

What is Salmonella dublin Fumarate Reductase Subunit D and what is its function?

Fumarate reductase subunit D (frdD) from Salmonella dublin is a hydrophobic membrane protein component of the fumarate reductase complex, which plays a critical role in anaerobic respiration. The protein consists of 119 amino acids and functions as part of the membrane anchor for the fumarate reductase enzyme complex . This complex catalyzes the reduction of fumarate to succinate, allowing the bacterium to use fumarate as a terminal electron acceptor during anaerobic growth conditions.

The full amino acid sequence of S. dublin frdD is: MINPNPKRSDEPVFWGLFGAGGMWGAIIAPVIVLLVGIMPLGLFPGDALSYERVLTFAQSFIGRVFLFLMIVLPLWCGLHRMHHAMHDLKIHVPAGKWVFYGLAAILTVVTAIGVITL . The protein is predominantly hydrophobic, consistent with its role as a membrane-spanning protein that anchors the catalytic components of the fumarate reductase complex.

How does the structure of frdD relate to its function in Salmonella dublin?

The structure of frdD is directly related to its function as a membrane anchor protein. Analysis of the amino acid sequence reveals multiple hydrophobic regions that form transmembrane helices spanning the bacterial inner membrane . These hydrophobic domains allow frdD to properly embed within the membrane bilayer, providing structural support for the catalytic subunits of the fumarate reductase complex.

The 13 kDa hydrophobic protein (as noted in alternative naming) contains specific regions that interact with other subunits of the fumarate reductase complex, particularly with the frdC subunit . These protein-protein interactions are essential for assembling the functional enzyme complex that catalyzes electron transfer from quinol to fumarate. The membrane-embedded nature of frdD also helps position the active site of the enzyme complex at the appropriate orientation relative to the electron transport chain components.

What expression systems are optimal for producing recombinant S. dublin frdD protein?

For optimal expression of recombinant Salmonella dublin frdD, E. coli-based expression systems are typically most effective, as demonstrated in similar membrane protein expression studies . When expressing hydrophobic membrane proteins like frdD, several considerations are paramount:

• Vector selection: Vectors with tightly controlled inducible promoters (such as T7 or araBAD) help manage the potentially toxic effects of membrane protein overexpression.

• E. coli strain selection: Strains specifically designed for membrane protein expression, such as C41(DE3) or C43(DE3), often yield better results than standard BL21(DE3) strains for hydrophobic proteins like frdD.

• Fusion tags: N-terminal His-tags have proven effective for purification of frdD-like proteins . The typical construct design involves adding the His-tag to the N-terminus of the full-length protein (amino acids 1-119 for S. dublin frdD).

• Growth conditions: Lowering the expression temperature to 16-25°C after induction and using a reduced inducer concentration often improves the yield of correctly folded membrane proteins.

The expression protocol should include careful optimization of induction timing, typically inducing at mid-log phase (OD600 of 0.6-0.8) to balance biomass and protein expression efficiency.

What experimental design considerations are crucial when studying S. dublin frdD in virulence research?

When designing experiments to study Salmonella dublin frdD in virulence research, several critical considerations must be addressed:

• Control selection: Proper experimental design requires careful selection of controls . For frdD studies, this should include:

  • Positive controls: Wild-type S. dublin strains with known virulence

  • Negative controls: S. dublin strains with frdD gene knockouts or mutations

  • Complementation controls: Knockout strains with reintroduced functional frdD

• Variables definition and control :

• Hypothesis formulation: Develop specific, testable hypotheses about frdD's role in virulence . For example: "Mutations in the transmembrane domains of frdD will reduce S. dublin virulence in BALB/c mice by disrupting anaerobic respiration during infection."

• Subject assignment: For in vivo virulence studies, use randomized assignment of animal subjects to treatment groups to minimize bias . Both between-subjects (different animals for each condition) and within-subjects (same animal tested under different conditions over time) designs may be appropriate depending on the specific research question.

• Genetic mapping integration: Incorporate knowledge about virulence plasmids in S. dublin, particularly how frdD relates to the virulence regions mapped on plasmids like pSDL2 . This may involve transposon mutagenesis or targeted gene replacement strategies to determine functional relationships.

How can researchers effectively purify and stabilize recombinant frdD for structural and functional studies?

Purification and stabilization of recombinant frdD present significant challenges due to its hydrophobic nature. An effective methodological approach includes:

• Membrane extraction optimization:

  • Solubilize bacterial membranes containing expressed frdD using a detergent screen to identify optimal extraction conditions

  • Recommended detergents include n-dodecyl-β-D-maltoside (DDM), lauryl maltose neopentyl glycol (LMNG), or digitonin at concentrations 2-3× their critical micelle concentration

  • Perform extraction at 4°C with gentle agitation for 1-2 hours

• Purification strategy:

  • Implement immobilized metal affinity chromatography (IMAC) using Ni-NTA resin for His-tagged frdD

  • Include detergent at concentrations above CMC in all purification buffers

  • Follow with size exclusion chromatography to separate protein-detergent complexes from aggregates

• Stabilization techniques:

  • Maintain protein in Tris-based buffer with 50% glycerol for long-term storage

  • For functional studies, reconstitute purified frdD into nanodiscs or liposomes to provide a membrane-like environment

  • Avoid repeated freeze-thaw cycles; store working aliquots at 4°C for up to one week

• Quality control assessments:

  • Verify protein purity via SDS-PAGE (>90% purity is desirable)

  • Confirm identity via mass spectrometry or Western blotting

  • Evaluate protein folding using circular dichroism spectroscopy

A systematic detergent screen is particularly important, as the choice of detergent significantly impacts both yield and activity of membrane proteins like frdD. The table below outlines recommended detergents and their applications:

What genomic approaches can be used to study frdD variation across Salmonella dublin strains?

Whole Genome Sequencing (WGS) has revolutionized the study of bacterial strains, including Salmonella dublin, offering superior resolution compared to traditional typing methods . For studying frdD variation across S. dublin strains, researchers should implement the following methodological approaches:

• DNA extraction and sequencing protocol:

  • Culture S. dublin strains on selective media such as XLD

  • Extract genomic DNA using standardized methods (e.g., KingFisher Duo Prime protocol)

  • Assess DNA quality using Qubit (quantity), Nanodrop (purity), and agarose gel electrophoresis (integrity)

  • Prepare NGS libraries using kits such as Nextera XT DNA Library Prep Kit

  • Perform paired-end sequencing (2 × 150 bases) on platforms like Illumina NextSeq500

• Bioinformatic analysis pipeline:

  • Quality control raw reads using tools like FastQC and Trimmomatic

  • Assemble genomes using SPAdes or Unicycler for high-quality draft assemblies

  • Annotate genomes using Prokka to identify frdD and related genes

  • Perform comparative genomics using tools like Roary to identify core and accessory genome components

  • Conduct phylogenomic analysis considering the impact of homologous recombination events on accuracy

• Specific frdD variant analysis:

  • Align frdD sequences across strains to identify SNPs and structural variations

  • Correlate variants with metadata including isolation date, geographical origin, and host information

  • Map variants onto protein structure models to predict functional impacts

  • Perform selection pressure analysis using dN/dS ratios to identify evolutionarily conserved regions

• Integration with phenotypic data:

  • Correlate genomic variants with virulence phenotypes in experimental models

  • Investigate associations between frdD variants and metabolic capabilities

  • Develop PCR-based assays targeting key variant regions for rapid strain typing

This comprehensive approach allows researchers to understand the evolutionary dynamics of frdD and its relationship to S. dublin strain virulence, geographical distribution, and adaptation to different environments.

How does frdD contribute to the virulence mechanisms of Salmonella dublin?

The contribution of frdD to Salmonella dublin virulence involves several complex mechanisms:

• Role in anaerobic metabolism during infection:

  • During intestinal colonization and within macrophage phagosomes, S. dublin encounters oxygen-limited environments

  • Fumarate reductase activity, dependent on functional frdD, enables anaerobic respiration using fumarate as a terminal electron acceptor

  • This metabolic adaptation provides energy for bacterial survival and replication in anoxic host environments

• Connection to virulence plasmids:

  • Plasmids of approximately 80 kb found in clinical isolates of S. dublin are essential for virulence

  • While frdD is typically chromosomally encoded, its expression and regulation may be influenced by virulence plasmid-encoded factors

  • The 80-kb plasmid pSDL2 is required for establishing lethal systemic infection in BALB/c mice

• Experimental evidence from virulence mapping:

  • Transposon insertion mutagenesis with Tn5-oriT has mapped virulence regions within plasmids

  • Similar approaches can determine whether disruption of chromosomal frdD affects virulence

  • In vivo infection models reveal that functional anaerobic respiration machinery is critical for full virulence

• Comparative virulence across Salmonella serovars:

  • S. dublin, S. enteritidis, and S. choleraesuis contain similar virulence plasmids with common elements

  • Comparative analysis of frdD across these serovars may reveal serotype-specific adaptations

  • Cross-complementation experiments can determine functional conservation of frdD across serovars

To experimentally investigate these mechanisms, researchers should employ:

• Gene knockout and complementation studies targeting frdD

• Growth curve analysis under aerobic versus anaerobic conditions

• Mouse infection models comparing wild-type and frdD mutant strains

• Transcriptomic analysis to identify co-regulated genes during infection

What are the key considerations for designing frdD mutation studies to assess functional impacts?

When designing mutation studies for Salmonella dublin frdD, researchers must carefully consider:

• Mutation strategy selection:

  • Site-directed mutagenesis: For targeted amino acid changes in specific functional domains

  • Deletion mutagenesis: To remove entire functional regions (e.g., transmembrane domains)

  • Random mutagenesis: For unbiased functional screening

  • CRISPR-Cas9 genome editing: For chromosomal modifications without leaving marker sequences

• Target site identification:

  • Prioritize conserved residues identified through multiple sequence alignment

  • Focus on transmembrane domains that anchor the protein in the membrane

  • Target residues involved in interactions with other fumarate reductase subunits

  • Consider the following key regions within the frdD sequence:

How can researchers effectively design experiments to study the role of frdD in bacterial metabolism?

Designing experiments to study frdD's role in bacterial metabolism requires a multifaceted approach:

• Growth condition optimization:

  • Compare growth under aerobic vs. anaerobic conditions

  • Test different terminal electron acceptors (fumarate, nitrate, DMSO)

  • Evaluate growth in minimal media with defined carbon sources

  • Determine optimal temperature, pH, and salt concentration ranges

• Metabolic flux analysis:

  • Use 13C-labeled substrates to track carbon flow through central metabolism

  • Measure exchange rates of key metabolites in wild-type vs. frdD mutants

  • Quantify changes in intracellular metabolite pools using LC-MS/MS

  • Develop a metabolic model incorporating fumarate reductase activity

• Respiratory chain analysis:

  • Measure membrane potential using fluorescent probes

  • Quantify ATP production under different respiratory conditions

  • Assess quinone reduction states in membrane preparations

  • Determine oxygen consumption rates and affinity

• Enzymatic activity assays:

  • Develop in vitro assays measuring fumarate reduction using purified components

  • Test enzyme kinetics with varying substrate concentrations

  • Assess inhibitor effects on enzyme activity

  • Compare native enzyme complexes versus reconstituted systems

• Experimental design considerations :

  • Implement a between-subjects design when comparing different bacterial strains

  • Use within-subjects design for testing the same strain under various conditions

  • Control for extraneous variables like media batch variation and growth phase

  • Design factorial experiments to test interactions between multiple variables

The following experimental protocol outline provides a framework for studying frdD's metabolic role:

• Generate defined frdD mutant and complemented strains

• Conduct growth curve analysis under various respiratory conditions

• Isolate membrane fractions for direct enzyme activity measurements

• Perform metabolomics analysis of central metabolites

• Integrate results to develop a comprehensive model of frdD's metabolic contributions

What approaches can be used to study protein-protein interactions involving frdD in the fumarate reductase complex?

Studying protein-protein interactions involving frdD requires specialized approaches suitable for membrane protein complexes:

• Co-immunoprecipitation with modifications for membrane proteins:

  • Crosslink proteins in intact cells using membrane-permeable crosslinkers

  • Solubilize membranes with gentle detergents that preserve protein-protein interactions

  • Use antibodies against frdD or epitope tags for immunoprecipitation

  • Identify interaction partners by mass spectrometry

• Bacterial two-hybrid systems for membrane proteins:

  • Utilize split-ubiquitin yeast two-hybrid system adapted for bacterial proteins

  • Employ BACTH (Bacterial Adenylate Cyclase Two-Hybrid) system with transmembrane domain accommodation

  • Screen for interactions with other fumarate reductase subunits and potential regulatory proteins

  • Quantify interaction strength using reporter gene expression

• Förster Resonance Energy Transfer (FRET) approaches:

  • Fuse fluorescent protein pairs to frdD and potential interaction partners

  • Measure FRET efficiency as indicator of protein proximity

  • Perform live-cell FRET measurements to capture dynamic interactions

  • Use acceptor photobleaching FRET for quantitative measurements

• Chemical crosslinking coupled with mass spectrometry (XL-MS):

  • Apply membrane-permeable crosslinkers with varying spacer lengths

  • Digest crosslinked complexes and analyze by liquid chromatography-tandem mass spectrometry

  • Identify crosslinked peptides using specialized search algorithms

  • Map interaction interfaces onto structural models

• Blue native PAGE and complex isolation:

  • Solubilize membrane complexes under native conditions

  • Separate intact complexes by blue native PAGE

  • Excise bands containing fumarate reductase complex

  • Identify components by second-dimension SDS-PAGE or mass spectrometry

The table below summarizes the advantages and limitations of each approach:

How should researchers interpret contradictory results in frdD functional studies?

When encountering contradictory results in functional studies of Salmonella dublin frdD, researchers should implement the following methodological approach:

• Systematic source identification:

  • Examine strain background differences that may contain suppressor mutations

  • Review experimental conditions, particularly oxygen availability during growth

  • Assess protein expression levels that might cause gain-of-function or dominant-negative effects

  • Consider post-translational modifications that vary between experimental systems

• Technical validation steps:

  • Sequence verify all strains to confirm genetic integrity

  • Implement multiple independent methodologies to test the same hypothesis

  • Quantify protein levels using Western blotting with appropriate loading controls

  • Verify membrane localization of frdD using fractionation experiments

• Statistical reassessment:

  • Increase sample sizes to improve statistical power

  • Apply appropriate statistical tests based on data distribution

  • Implement blinded analysis to reduce unconscious bias

  • Consider Bayesian approaches to integrate prior knowledge with new data

• Integrated experimental design:

  • Reconcile contradictions by testing intermediate conditions

  • Design experiments that directly test competing hypotheses

  • Implement genetic suppressor screens to identify compensatory pathways

  • Develop more sensitive assays that can detect subtle phenotypic differences

• Literature contextualization:

  • Compare results with studies on homologous proteins in related organisms

  • Consider evolutionary context that might explain functional differences

  • Examine whether contradictions reflect biologically meaningful strain variations

  • Consult experts in the field for alternative interpretations

When interpreting contradictory data, researchers should construct a decision tree that guides further experimentation rather than prematurely dismissing results that don't align with expectations. This iterative approach helps distinguish between genuine biological complexity and technical artifacts.

What bioinformatic approaches are most effective for analyzing frdD sequence variation and evolutionary relationships?

Effective bioinformatic analysis of frdD sequence variation requires a multi-faceted approach:

• Sequence acquisition and alignment:

  • Retrieve frdD sequences from public databases including NCBI, UniProt, and specialized Salmonella databases

  • Perform multiple sequence alignment using algorithms optimized for membrane proteins (e.g., MAFFT with --localpair option)

  • Manually inspect and refine alignments, particularly in transmembrane regions

  • Consider using codon-aware alignment algorithms for downstream selection analysis

• Phylogenetic analysis:

  • Select appropriate evolutionary models using ModelTest or similar tools

  • Construct phylogenetic trees using maximum likelihood or Bayesian approaches

  • Assess node support through bootstrap analysis or posterior probabilities

  • Consider the impact of homologous recombination events on phylogenomic reconstructions

  • Compare topologies from different tree construction methods

• Selection pressure analysis:

  • Calculate dN/dS ratios across the sequence to identify regions under selection

  • Implement site-specific selection tests using PAML or HyPhy

  • Test for episodic selection using methods like MEME

  • Correlate selection patterns with functional domains

• Structural prediction and annotation:

  • Predict transmembrane topology using multiple algorithms (TMHMM, Phobius)

  • Generate 3D structural models using approaches specialized for membrane proteins

  • Map sequence conservation onto structural models using ConSurf or similar tools

  • Identify co-evolving residues using mutual information analysis

• Comparative genomics integration:

  • Examine genomic context of frdD across strains

  • Identify synteny patterns and operon structure conservation

  • Investigate horizontal gene transfer events

  • Correlate genomic findings with isolation metadata including geographical origin and isolation date

This comprehensive bioinformatic framework allows researchers to extract maximum biological insight from sequence data, connecting evolutionary patterns to functional implications and strain-specific adaptations.