Recombinant Edwardsiella tarda Protein AaeX (aaeX)

What is the structural and biochemical profile of Recombinant Edwardsiella tarda Protein AaeX?

Recombinant Edwardsiella tarda Protein AaeX (aaeX) is derived from the Edwardsiella tarda strain EIB202. According to available data, this protein has the following characteristics:

• Uniprot Accession Number: D0ZF88

• Gene Name: aaeX

• Ordered Locus Names: ETAE_3128

• Expression Region: 1-67

• Amino Acid Sequence: MGTLPVMVLFGLSFPPAFFALLAALPLFWLLRRLLQPSGLYDMIWHPALFNCALYGCLFY LVSWLFI

The sequence analysis reveals a relatively small protein with multiple hydrophobic residues (leucine, phenylalanine, alanine), suggesting it may have membrane-associated properties. The protein appears to be supplied in a Tris-based buffer with 50% glycerol, optimized for stability . For experimental work, it's important to note that repeated freezing and thawing is not recommended, and while the protein should be stored at -20°C (or -80°C for extended storage), working aliquots can be maintained at 4°C for up to one week .

What are the optimal expression systems for producing Recombinant Edwardsiella tarda Protein AaeX?

While specific optimization studies for AaeX expression are not directly addressed in the available literature, several expression systems can be employed based on general recombinant protein production principles and experiences with other E. tarda proteins.

For bacterial proteins like AaeX, the E. coli expression system is typically the first choice due to its efficiency and cost-effectiveness. This approach has been successfully used for other E. tarda recombinant proteins including HflC, HflK, and YhcI, which were "over-expressed and purified using the E. coli expression system" . When designing an expression strategy for AaeX, researchers should consider:

• Selection of appropriate E. coli strains (BL21(DE3), Rosetta)

• Vector systems with strong, inducible promoters

• Inclusion of affinity tags to facilitate purification

• Optimization of culture conditions (temperature, induction timing, media composition)

Alternative expression systems including yeast, baculovirus, and mammalian cell systems may be considered if E. coli expression results in insoluble protein or if post-translational modifications are required . The choice should be guided by the intended experimental application and the required protein characteristics.

How should researchers verify the identity and functionality of purified Recombinant Edwardsiella tarda Protein AaeX?

Verification of recombinant AaeX requires a multi-faceted approach combining analytical techniques to confirm both identity and functionality:

Identity verification should include:

• SDS-PAGE analysis to confirm the expected molecular weight

• Western blotting using specific antibodies, similar to the approach where "purified proteins were immunoblotted with rabbit's sera and the immunoreactivity of the purified proteins was found to be specific"

• Mass spectrometry for definitive sequence confirmation

• N-terminal sequencing if necessary

For purity assessment, the protein should meet or exceed the standard of "greater or equal to 85% purity as determined by SDS-PAGE" . This typically requires multiple purification steps, potentially including:

• Affinity chromatography (if tagged)

• Ion exchange chromatography

• Size exclusion chromatography as a final polishing step

Functional verification would depend on the predicted role of AaeX, which is not fully characterized in the available literature. Possible approaches include:

• Binding assays if AaeX is suspected to interact with host molecules

• Cell-based assays measuring effects on host cells (adhesion, invasion)

• Immunological assays to test immunogenicity

• Structural analysis using circular dichroism or other biophysical techniques

A comprehensive verification strategy combining these approaches ensures that the recombinant protein faithfully represents native AaeX and is suitable for subsequent experimental applications.

What are the key considerations for designing differential expression studies involving AaeX?

Differential expression studies involving AaeX require careful experimental design to generate reliable and interpretable data. Based on established methodologies for such studies, researchers should consider:

Experimental design factors:

• Define clear research questions about AaeX expression under different conditions

• Include appropriate control and experimental groups with sufficient biological replicates (minimum of 3 per condition)

• Consider time-course experiments if temporal expression changes are of interest

• Account for potential confounding variables

For transcriptomic analysis of aaeX expression, RNA-Seq or quantitative RT-PCR methodologies are appropriate, while proteomic analysis would utilize mass spectrometry-based approaches. In either case, data analysis should include:

• Normalization to account for technical variations

• Batch effect correction if necessary

• Statistical analysis using appropriate tools (e.g., DESeq2 for RNA-Seq)

• Visualization of results using volcano plots and heatmaps

As noted in the literature: "DiffExp presents the differential expression analysis results in a volcano plot and an interactive table... In the volcano plot, upregulated genes/proteins are highlighted red, and downregulated ones are highlighted blue" . These visualization approaches help identify significant expression changes that might indicate important conditions affecting AaeX expression or function.

Validation experiments should confirm findings using alternative techniques, and downstream functional analysis can connect expression changes to biological phenotypes, providing a comprehensive understanding of AaeX regulation and function.

How should immunization protocols be designed to evaluate AaeX as a potential vaccine candidate?

Designing immunization studies to evaluate AaeX's potential as a vaccine candidate should follow established immunological research methodologies, comparable to those used for other E. tarda immunogenic proteins. Based on successful approaches with other E. tarda proteins, a systematic protocol should include:

Vaccine preparation:

• Express and purify recombinant AaeX to ≥85% purity

• Determine protein concentration using reliable methods (e.g., BCA assay)

• Formulate with appropriate adjuvants (e.g., Alhydrogel as mentioned: "100 μg of protein in Alhydrogel was used as a vaccine formulation")

Immunization schedule:

• Primary immunization with complete adjuvant

• Booster doses at appropriate intervals (e.g., "The second booster of 100 μg protein was administered by subcutaneous injections on the 21st day")

• Control groups receiving adjuvant alone or established vaccines

Immune response assessment:

• Serum collection at defined intervals (pre-immunization, post-primary, post-boosters)

• Antibody titer determination using ELISA

• Cytokine profiling (particularly IL-10, IFN-γ)

• T-helper cell response analysis (Th1/Th2 balance)

Challenge studies:

• Challenge with virulent E. tarda strain (typically "5x median lethal dose (LD50)")

• Monitor survival rates, clinical signs, and bacterial loads

• Compare protection with established vaccine candidates

The methodology should be designed to determine if AaeX can "induce a strong immune response upon infection and elicit the significant production of IL-10, IFN-γ, Th1, and Th2 mediated mRNA expression," which would indicate potential effectiveness as a vaccine candidate .

What methodology should be used to identify potential interactions between AaeX and other bacterial or host proteins?

Identifying protein-protein interactions involving AaeX requires a multi-technique approach to ensure robust and reproducible results. Based on established methodologies in protein interaction studies, researchers should consider:

In vitro techniques:

• Pull-down assays using tagged recombinant AaeX as bait

• Co-immunoprecipitation with anti-AaeX antibodies

• Surface plasmon resonance (SPR) to measure binding kinetics

• Crosslinking studies to capture transient interactions

Genetic approaches:

• Bacterial two-hybrid systems

• Suppressor screening to identify compensatory mutations

• Co-expression analysis to identify genes with similar expression patterns

In silico methods:

• Homology-based prediction of interaction partners

• Structural modeling to identify potential binding interfaces

• Network analysis to place AaeX in context of known bacterial interactomes

Validation approaches:

• Reciprocal pull-downs (if A pulls down B, B should pull down A)

• Domain mapping to identify specific interaction regions

• Functional assays to determine biological relevance of interactions

For data analysis, tools for visualizing protein-protein interaction networks can be valuable, as they allow researchers to "navigate to PPIExp to visualize the PPI network of these genes" . After identifying interaction partners, functional enrichment analysis can help understand the biological context of these interactions.

Importantly, all experiments should include appropriate controls: positive controls (known interacting pairs), negative controls (proteins unlikely to interact with AaeX), and technical controls (e.g., tag-only controls for pull-downs) to ensure specificity and reliability of the identified interactions.

How does AaeX compare to other immunoreactive proteins of E. tarda in terms of vaccine potential?

While specific comparative studies on AaeX's vaccine potential are not directly available in the literature, we can establish a framework for comparison based on what is known about other E. tarda immunoreactive proteins. The immunoreactive proteins HflC, HflK, and YhcI have been characterized as having protective efficacy with "~60% survivability" in challenge experiments .

For a systematic comparison of AaeX with these established immunoreactive proteins, researchers would need to evaluate several parameters:

Immunogenicity profile:

• Antibody production levels following immunization

• Antibody subtype distribution

• Duration of antibody response

• Cross-reactivity with various E. tarda strains

Protective efficacy metrics:

• Survival rates following challenge

• Bacterial clearance efficiency

• Protection against different bacterial doses

• Cross-protection against heterologous strains

Immune response characteristics:

• Cytokine induction patterns, particularly "IL-10, IFN-γ, Th1, and Th2 mediated mRNA expression"

• Memory cell generation

• Mucosal immunity development (important for fish pathogens)

What role might AaeX play in E. tarda's antibiotic resistance mechanisms?

Edwardsiella tarda is known to be "naturally resistant to benzylpenicillin, oxacillin, macrolide, lincosamides, streptogramins, and glycopeptides" , making antibiotic treatment challenging. While the specific role of AaeX in this resistance profile is not directly documented in the available literature, a systematic research approach could elucidate its potential contributions.

To investigate AaeX's role in antibiotic resistance, researchers should consider:

Gene expression analysis:

• Compare aaeX expression levels between antibiotic-exposed and unexposed bacteria

• Evaluate expression in resistant versus susceptible strains

• Analyze co-expression patterns with known resistance genes

Functional studies:

• Generate aaeX knockout or knockdown strains

• Determine minimum inhibitory concentrations (MICs) for various antibiotics in wild-type versus mutant strains

• Complement mutants to confirm phenotype specificity

• Overexpress AaeX to evaluate effects on resistance profiles

Mechanistic investigations:

• Assess membrane permeability in the presence/absence of AaeX

• Evaluate efflux pump activity

• Measure biofilm formation capacity

• Analyze stress response pathways

The amino acid sequence of AaeX with its hydrophobic regions suggests potential membrane association , which could implicate it in permeability barriers or efflux systems. If AaeX contributes to the "difficulty of antibiotic-based treatment" , it might represent a novel target for adjunctive therapies designed to enhance antibiotic efficacy against E. tarda infections.

How might AaeX contribute to E. tarda's adhesion and invasion mechanisms?

E. tarda pathogenicity relies significantly on adhesion and invasion mechanisms, with multiple genes identified as contributing to these processes . While AaeX is not specifically mentioned among the characterized adhesion and invasion genes, its potential role can be systematically investigated based on its properties and expression patterns.

The hydrophobic character of AaeX suggested by its amino acid sequence could indicate membrane association, potentially contributing to:

Surface interactions:

• Cell surface hydrophobicity modulation

• Host receptor binding

• Biofilm formation facilitation

• Membrane vesicle formation or function

Invasion process involvement:

• Phagosome escape

• Intracellular survival

• Host cell signaling manipulation

• Stress response during invasion

To experimentally investigate AaeX's contribution to these processes, researchers should consider:

• Adhesion assays comparing wild-type and aaeX mutant strains

• Invasion efficiency measurements using cell culture models

• Localization studies to determine AaeX distribution during infection

• Protein interaction studies to identify binding to host components

Comparison with established virulence factors would be valuable, including "Pili/TIVSS- and fimbria-related genes, invasin and other putative virulence-related genes" . If AaeX functionally interacts with or complements these known virulence factors, it may represent an additional target for intervention strategies against E. tarda infections.

How can CRISPR-Cas9 gene editing be applied to study AaeX function in E. tarda?

CRISPR-Cas9 technology offers powerful approaches for precise genetic manipulation to elucidate AaeX function in E. tarda. While specific CRISPR studies on AaeX are not described in the available literature, a methodical research strategy can be outlined based on established genetic manipulation principles.

For comprehensive functional analysis of AaeX using CRISPR-Cas9, researchers should consider:

Gene knockout strategies:

• Design guide RNAs targeting the aaeX gene with minimal off-target effects

• Create clean deletion mutants to eliminate AaeX expression

• Confirm knockouts through genomic PCR, RT-PCR, and Western blotting

• Create complemented strains to verify phenotype specificity

Targeted modifications:

• Engineer point mutations in critical domains to study structure-function relationships

• Create truncated versions to identify essential regions

• Generate epitope-tagged versions for localization and interaction studies

• Introduce reporter fusions for expression analysis

Phenotypic characterization of mutants:

• Growth profiles under various conditions

• Antibiotic susceptibility patterns

• Virulence in cell culture and animal models

• Biofilm formation capacity

• Stress response characteristics

The CRISPR approach offers advantages over traditional mutagenesis methods, including precision, efficiency, and the ability to create marker-free mutations. This is particularly valuable when studying genes that may have subtle phenotypes or that function in complex genetic networks. The resulting mutants could be analyzed using differential expression approaches as described in the literature, where "DiffExp presents the differential expression analysis results in a volcano plot and an interactive table" , helping identify genes affected by AaeX deletion.

What systems biology approaches would be most effective for understanding AaeX's role in E. tarda pathogenesis?

Systems biology offers integrative approaches to understand AaeX's role within the broader context of E. tarda pathogenesis. To effectively apply systems biology to AaeX research, multiple data types and analytical approaches should be combined:

Multi-omics integration:

• Genomics: Compare aaeX sequence and context across strains with varying virulence

• Transcriptomics: Map expression patterns of aaeX and co-regulated genes under infection-relevant conditions

• Proteomics: Identify AaeX interaction partners and post-translational modifications

• Metabolomics: Determine metabolic changes associated with aaeX mutation

Network analysis:

• Construct protein-protein interaction networks centered on AaeX

• Identify pathways and functional modules incorporating AaeX

• Compare network properties with those of other pathogenic bacteria

• Predict functional relationships based on network topology

Host-pathogen interaction analysis:

• Map temporal dynamics of host responses to AaeX-expressing bacteria

• Identify host pathways perturbed by AaeX

• Compare with responses to known virulence factors

Data integration and visualization tools like those described in the literature would be valuable: "DiffExp also provides further operations to explore these differentially-expressed genes/proteins. Users can navigate to PPIExp to visualize the PPI network of these genes, navigate to KeggExp to visualize their interested KEGG pathways" .

This integrative approach would place AaeX within the broader context of E. tarda pathogenesis, potentially revealing new therapeutic targets and intervention strategies. The systems perspective is particularly valuable for understanding proteins like AaeX that may serve as nodes connecting multiple pathogenic processes.

What challenges exist in developing multi-epitope vaccines that incorporate AaeX epitopes?

Developing multi-epitope vaccines that include AaeX presents several significant challenges that researchers must systematically address. While specific studies on AaeX in multi-epitope vaccines are not described in the available literature, we can outline the methodological challenges based on vaccine development principles and experiences with other E. tarda proteins.

Epitope identification and optimization:

• Predicting immunogenic epitopes within AaeX using computational tools

• Experimentally validating epitope immunogenicity

• Assessing epitope conservation across E. tarda strains

• Ensuring epitopes do not cross-react with host proteins

Construct design considerations:

• Determining optimal arrangement of multiple epitopes (including AaeX epitopes)

• Selecting appropriate linkers between epitopes

• Maintaining natural epitope conformation within the chimeric construct

• Ensuring efficient expression and proper folding

Immunological challenges:

• Achieving balanced immune responses to all included epitopes

• Preventing immunodominance of certain epitopes over others

• Directing responses toward protective rather than non-protective epitopes

• Ensuring appropriate Th1/Th2 balance, as "HflC, HflK, and YhcI recombinant proteins evoke a highly protective effect against E. tarda challenge" partly through their ability to "elicit significant IL-10, IFN-γ, Th1, and Th2 mediated mRNA expression"

Delivery system optimization:

• Selecting appropriate adjuvants compatible with the multi-epitope construct

• Developing delivery systems suitable for the target species (fish, in many cases)

• Ensuring stability under various storage and administration conditions

• Optimizing dosage and administration protocols

A systematic approach to these challenges, including comparative studies with established vaccine antigens like HflC (identified as "a promising vaccine candidate against edwardsiellosis" ), could lead to the development of effective multi-epitope vaccines incorporating AaeX epitopes, potentially providing broader protection against E. tarda infections.

What statistical approaches are most appropriate for analyzing differential expression data involving AaeX?

Analyzing differential expression data involving AaeX requires robust statistical methods to account for biological variability and technical noise. Based on established bioinformatics practices described in the literature, researchers should consider:

Preprocessing and normalization:

• Quality control of raw data (sequence reads for RNA-Seq, spectral data for proteomics)

• Normalization to account for library size differences and batch effects

• Transformation of data to meet assumptions of statistical tests

Statistical testing framework:

• Appropriate models for count data (negative binomial for RNA-Seq)

• Multiple testing correction (e.g., Benjamini-Hochberg procedure)

• Effect size estimation (fold change) alongside statistical significance

• Visualization using volcano plots where "upregulated genes/proteins are highlighted red, and downregulated ones are highlighted blue"

Sensitivity analysis:

• Testing robustness of results to different normalization methods

• Evaluating the impact of outlier removal

• Assessing the effect of different significance thresholds

Contextual interpretation:

• Functional enrichment analysis of differentially expressed genes

• Pathway analysis to identify biological processes affected

• Network analysis to place AaeX in a functional context

As noted in the literature, tools that allow researchers to "change differential cut-offs, namely the P-value or adjusted P-value and fold-change" are valuable for exploring the sensitivity of results to different thresholds. The ability to visualize results directly and perform downstream analyses like "functional enrichment analysis for the upregulated or downregulated genes/proteins" facilitates comprehensive interpretation of AaeX's role in different experimental conditions.

How should researchers interpret contradictory results between in vitro and in vivo experiments involving AaeX?

Contradictions between in vitro and in vivo results are common in biological research and require careful interpretation. When studying AaeX function, researchers should apply a systematic approach to reconciling such contradictions:

Methodological reconciliation:

• Examine differences in experimental conditions (temperature, pH, oxygen levels)

• Compare protein concentrations and exposure durations

• Assess the relevance of in vitro models to in vivo environments

• Consider the complexity of in vivo systems versus reductionist in vitro approaches

Biological contextual factors:

• Host immune status in vivo versus absent/simplified immunity in vitro

• Interactions with microbiota present in vivo but absent in vitro

• Tissue-specific responses that cannot be modeled in simple cell cultures

• Dynamic temporal aspects of infections versus static in vitro conditions

Data integration approaches:

• Develop mathematical models that can incorporate both in vitro and in vivo data

• Use systems biology approaches to contextualize contradictory findings

• Perform intermediate complexity experiments (ex vivo, organoid) to bridge the gap

• Conduct dose-response studies to identify threshold effects

When interpreting seemingly contradictory results, researchers should consider that in vitro experiments may reveal mechanistic details but lack physiological context, while in vivo experiments provide physiological relevance but may obscure specific mechanisms. For example, if AaeX shows immunogenic properties in vitro but limited protection in vivo, factors like "survivability" metrics and immune response parameters should be carefully evaluated to understand the discrepancy.

What bioinformatic tools are most useful for predicting AaeX structure and potential functions?

Predicting the structure and functions of AaeX requires specialized bioinformatic tools that can analyze sequence features, structural properties, and evolutionary relationships. Based on current bioinformatic practices, researchers should consider:

Sequence analysis tools:

• Homology detection (BLAST, HHpred, HMMER) to identify related proteins

• Multiple sequence alignment to identify conserved residues

• Motif recognition to identify functional domains

• Transmembrane topology prediction (given AaeX's likely membrane association based on its sequence )

Structural prediction approaches:

• Secondary structure prediction (PSIPRED, JPred)

• Ab initio modeling for novel structures

• Homology modeling if structural homologs exist

• Molecular dynamics simulations to explore conformational flexibility

Functional annotation tools:

• Gene Ontology term prediction

• Pathway mapping tools

• Protein-protein interaction prediction

• Ligand binding site prediction

Evolutionary analysis:

• Phylogenetic analysis to place AaeX in evolutionary context

• Detection of selective pressure signatures

• Identification of co-evolving residues indicating functional importance

Integration with experimental data:

• Incorporating differential expression data as described in the literature

• Using protein-protein interaction data to refine functional predictions

• Validating predictions through targeted mutagenesis

The combination of these computational approaches can generate testable hypotheses about AaeX structure and function, guiding experimental design. For instance, if structural predictions suggest a potential interaction interface, this could be validated through the protein-protein interaction visualization approaches described in the literature, where researchers can "navigate to PPIExp to visualize the PPI network of these genes" .