Recombinant Escherichia coli Elongation factor G (fusA)

What expression systems are available for producing Recombinant Escherichia coli Elongation factor G (fusA)?

Recombinant Escherichia coli Elongation factor G (fusA) can be produced using various expression systems, including E. coli, yeast, baculovirus, and mammalian cell cultures . Each system offers distinct advantages depending on your research needs. E. coli-based expression (product code CSB-EP009373ENX) provides high yields for structural studies, while mammalian cell expression (CSB-MP009373ENX) may be preferred when studying eukaryotic interactions . For applications requiring biotinylation, specialized versions such as Avi-tag Biotinylated fusA are available, where E. coli biotin ligase (BirA) catalyzes the amide linkage between biotin and a specific lysine of the AviTag peptide .

How does the expression source affect the functionality of recombinant fusA protein?

The expression source significantly impacts fusA protein folding, post-translational modifications, and functional characteristics. E. coli-expressed fusA typically has minimal post-translational modifications but maintains core elongation factor activity . Yeast and baculovirus systems may introduce eukaryotic-type modifications that can affect protein-protein interactions . When selecting an expression system, researchers should consider the intended application, required purity, and whether specific modifications are needed for functional studies.

What techniques are most effective for creating fusA knockout mutants?

For creating fusA knockout mutants, homologous recombination using the red recombination system has proven highly effective . The process involves designing primers with 20 bp homologous sequences flanking the fusA gene, fused with sequences for a tetracycline resistance marker . After amplification and purification of the resistance gene fragment, the construct is transformed into bacteria containing the pKD46 plasmid, which expresses the recombination enzymes Exo, Bet, and Gam when induced with L-arabinose . Positive transformants can be selected on tetracycline-containing media and validated through PCR and gene sequencing . This approach allows precise gene deletion while minimizing polar effects on neighboring genes.

How should growth curve analysis be performed to evaluate fusA mutant phenotypes?

When performing growth curve analysis to evaluate fusA mutant phenotypes, cultures should be standardized to equal starting optical densities (approximately OD600 = 0.3) . Measurements should be taken hourly for at least 24 hours to capture the complete growth cycle . Include wild-type strains as controls, and ensure at least three independent biological replications for statistical validity . Growth parameters to analyze include lag phase duration, exponential growth rate, and maximum cell density. Statistical comparison between wild-type and mutant strains will reveal how fusA mutation affects bacterial growth kinetics, providing insights into its role in cellular metabolism and protein synthesis.

How does fusA contribute to bacterial virulence mechanisms?

Studies suggest that fusA plays a crucial role in bacterial virulence mechanisms, particularly in pathogens like Pseudomonas plecoglossicida . Dual RNA-seq analysis has revealed that fusA might be an important virulence gene during infection processes . When fusA is knocked out, bacteria exhibit altered growth patterns, reduced adhesion capabilities, compromised biofilm formation, and diminished environmental adaptation . These phenotypic changes collectively contribute to attenuated virulence. The precise molecular mechanisms linking fusA to these virulence factors likely involve its primary function in protein synthesis, affecting the expression of multiple downstream virulence-associated proteins.

How can Group Association Model (GAM) improve analysis of fusA mutation effects on antibiotic resistance?

The Group Association Model (GAM) offers a sophisticated approach for analyzing fusA mutation effects on antibiotic resistance, particularly for fusidic acid resistance . GAM addresses challenges in traditional genome-wide association studies by segregating isolates based on shared drug-resistance profiles, thereby increasing data dimensionality . This method effectively reduces false positive associations by accounting for cross-resistance artifacts that frequently confound conventional Linear Mixed Models (LMM) . GAM has successfully identified SNP resistance-associated mechanisms for fusidic acid with greater precision than traditional approaches . For researchers studying fusA mutations in relation to antibiotic resistance, implementing GAM can significantly improve the reliability of genotype-phenotype associations.

What strategies help resolve contradictions in fusA genotype-phenotype data analysis?

Resolving contradictions in fusA genotype-phenotype data requires a multifaceted approach. A structured notation of contradiction patterns using parameters (α, β, θ) can help classify the complexity of interdependencies, where α represents the number of interdependent items, β indicates contradictory dependencies defined by domain experts, and θ reflects the minimal required Boolean rules . This framework helps handle multidimensional interdependencies within datasets . For fusA research specifically, implementing machine learning with Group Association Models can further refine analysis by reducing false positives and accurately identifying true gene-drug associations even with missing data . Cross-validation using different sample sizes and random test sets ensures robust results and minimizes contradictions in experimental findings .

How can fusA be utilized in sound source recognition for environmental monitoring?

Though seemingly unrelated to molecular biology, fusA research methodology has parallels with machine learning approaches used in environmental monitoring systems like FuSA (Integrated System for the Analysis of Environmental Sound Sources) . Both domains employ deep neural models for pattern recognition and classification . Researchers can apply similar machine learning approaches used in FuSA environmental sound analysis to biological data from fusA experiments . For example, recurrent neural networks (RNNs) that classify urban sound sources could be adapted to classify bacterial phenotypes resulting from fusA mutations . This cross-disciplinary approach demonstrates how methodologies from distinct fields can inform novel analytical techniques in molecular biology research.

What considerations are important when designing experiments to study fusA's role in antibiotic resistance mechanisms?

When designing experiments to study fusA's role in antibiotic resistance mechanisms, researchers should implement a comprehensive approach that accounts for the complexity of genotype-phenotype relationships. The Group Association Model (GAM) has identified SNP resistance-associated mechanisms for fusidic acid with greater accuracy than traditional methods . Experimental designs should include diverse bacterial strains with varying resistance profiles to increase statistical power . Sample size significantly impacts detection of true positive associations, with larger datasets substantially reducing false positives . Additionally, researchers should implement machine learning approaches to handle missing data, as shown by ML-GAM workflows that maintain accuracy even with incomplete datasets . Control for cross-resistance artifacts by carefully selecting comparison groups and validating findings through phenotypic testing.

What are the commonly available recombinant fusA products for research?

Table compiled based on available product information

How do fusA mutation effects compare across different experimental models?

Table compiled based on research findings across cited studies

What emerging technologies show promise for advanced fusA functional studies?

Emerging technologies for fusA functional studies include cryo-electron microscopy for high-resolution structural analysis, CRISPR-Cas9 for precise genome editing to create specific fusA variants, and advanced machine learning models like those used in Group Association Models for complex data analysis . FuSA's deep neural network approach to sound classification demonstrates how transfer learning could be applied to biological datasets for improved pattern recognition . Single-cell techniques could reveal cell-to-cell variability in fusA expression and function, while high-throughput phenotypic screening would accelerate discovery of novel fusA-targeting compounds. Integration of these technologies promises to advance our understanding of fusA's multifaceted roles in bacterial physiology and pathogenesis.

How might contradictions in fusA research data be systematically addressed in future studies?

Future studies addressing contradictions in fusA research data would benefit from implementing structured contradiction pattern analysis using the (α, β, θ) parameter framework, where α represents interdependent items, β indicates contradictory dependencies, and θ reflects required Boolean rules . This approach allows systematic classification of contradiction patterns across multiple domains . Researchers should also employ machine learning enhanced Group Association Models (ML-GAM) that have demonstrated superior accuracy in handling complex biological datasets with missing data . Cross-validation using multiple random test sets will ensure robust results . Additionally, developing standardized metadata schemas specifically for fusA studies would facilitate data integration across research groups and enable more effective contradiction detection and resolution.