Recombinant Ureaplasma urealyticum serovar 10 Elongation factor G (fusA), partial

What is Ureaplasma urealyticum serovar 10 and how is it taxonomically classified?

Ureaplasma urealyticum serovar 10 is one of the ten recognized serovars of U. urealyticum, which belongs to the family Mycoplasmataceae. U. urealyticum is distinguished from the closely related U. parvum (which has 4 serovars) through specific genetic markers. The taxonomic classification is significant because different Ureaplasma species and serovars exhibit varying pathogenicity and antimicrobial susceptibility profiles. Modern classification requires molecular techniques as these organisms lack cell walls and have limited biochemical activity, making traditional bacterial identification methods unsuitable .

What is the function of Elongation factor G (fusA) in Ureaplasma and how does it compare to other bacterial species?

Elongation factor G (encoded by the fusA gene) plays a critical role in the translocation step of bacterial protein synthesis. In Ureaplasma urealyticum serovar 10, as in other bacteria, EF-G catalyzes the movement of mRNA and tRNAs on the ribosome during translation elongation. This protein is highly conserved across bacterial species, making it useful for phylogenetic studies and as a potential antimicrobial target. The fusA gene in U. urealyticum serovar 10 shows high conservation (>99.97% identity) across all ten U. urealyticum serovars, making it a reliable species-specific marker for differentiation from U. parvum . While the core function remains similar across bacterial species, subtle structural variations in EF-G can influence antimicrobial susceptibility, particularly to drugs that target protein synthesis.

Why is studying recombinant forms of U. urealyticum proteins important for research?

Studying recombinant forms of U. urealyticum proteins, including Elongation factor G, is crucial because:

• Native protein isolation is challenging due to the fastidious growth requirements and low protein yields from Ureaplasma cultures

• Recombinant expression enables structure-function analyses through site-directed mutagenesis

• Purified recombinant proteins can be used to develop more specific diagnostic assays

• They provide materials for antimicrobial susceptibility testing and drug development

• They enable detailed biochemical characterization without the confounding effects of other cellular components

Recombinant protein expression typically involves cloning the fusA gene into expression vectors, transforming into E. coli or other host systems, and optimizing expression conditions to produce functional protein for downstream applications .

What are the most reliable molecular methods for detecting and differentiating Ureaplasma urealyticum serovar 10?

The most reliable molecular methods for detecting and differentiating U. urealyticum serovar 10 include:

• Species-specific real-time PCR: Using primers targeting the 15,072-bp open reading frame that is highly conserved (>99.97%) across all ten U. urealyticum serovars but distinct from U. parvum .

• Serovar-specific PCR assays: Primers and probes designed based on unique genomic regions identified through whole-genome sequence comparison among all 14 Ureaplasma serovars .

• Multiplexed PCR approaches: These allow simultaneous detection of multiple Ureaplasma serovars, improving efficiency and reducing cost.

The methodology involves careful primer design targeting regions with <80% identity matches to other serovars, followed by validation using ATCC type strains. For U. urealyticum serovar 10, specificity is achieved using Simple Probe Chemistry (SPC) probes, which provide better discrimination than primers alone. These molecular approaches represent significant improvements over earlier methods that relied on the more variable mba gene and offer complete separation of all 14 Ureaplasma serovars .

How can researchers accurately quantify fusA gene expression in Ureaplasma urealyticum clinical isolates?

Accurate quantification of fusA gene expression in U. urealyticum clinical isolates involves several methodological considerations:

• Sample collection and processing: Proper collection of clinical specimens (urethral or cervical swabs) with immediate stabilization of RNA to prevent degradation.

• RNA extraction optimization: Using specialized kits designed for difficult-to-lyse bacteria with minimal genomic DNA contamination.

• Reverse transcription quantitative PCR (RT-qPCR): Designing primers specific to the fusA gene of U. urealyticum with appropriate reference genes for normalization.

• Absolute quantification: Generating standard curves using recombinant plasmids containing the target fusA sequence.

• Relative quantification: Normalizing expression using multiple reference genes that maintain stable expression under various conditions.

• Correction for bacterial load: Similar to the approach used for Ureaplasma detection in clinical samples, where bacterial quantification is normalized to host cell numbers to correct for sampling variations .

For accurate results, researchers should include appropriate controls and validate the specificity of primers against all 14 Ureaplasma serovars to avoid cross-reactivity.

What approaches are most effective for distinguishing between commensal colonization and pathogenic infection with U. urealyticum?

Distinguishing between commensal colonization and pathogenic infection with U. urealyticum requires multifaceted approaches:

• Quantitative assessment: Higher bacterial loads (>10 copies/1000 cells) have been associated with pathogenic potential rather than mere colonization . Studies show that U. urealyticum at high densities has a relative risk of 3.131 (p<0.05) for non-specific cervicitis compared to controls .

• Inflammatory markers assessment: Measuring polymorphonuclear leukocytes (PMNL) in urethral smears, where severe inflammation (>30 PMNL/HPF) correlates with U. urealyticum infection in some cases .

• Clinical correlation: Evaluating symptoms alongside laboratory findings, as some studies show that U. urealyticum can cause less severe urethral inflammation than C. trachomatis and M. genitalium .

• Multi-pathogen testing: Excluding other STIs before attributing symptoms to U. urealyticum, as co-infections are common .

• Serovar determination: Different serovars may have different pathogenic potential, with serovar 3/14 being identified more frequently in some clinical presentations .

This multi-parameter approach helps avoid overtreatment of commensal colonization while ensuring appropriate management of true infections.

What expression systems are optimal for producing recombinant U. urealyticum Elongation factor G?

The optimal expression systems for producing recombinant U. urealyticum Elongation factor G include:

• E. coli expression systems:

  • BL21(DE3) strains with pET vector systems for high-level expression

  • Rosetta or CodonPlus strains to address codon bias issues in Ureaplasma genes

  • Fusion tags (His, GST, MBP) to enhance solubility and facilitate purification

• Cell-free expression systems:

  • Useful for potentially toxic proteins

  • Allows immediate access to the synthesized protein

  • Can incorporate modified amino acids for structural studies

• Yeast expression systems:

  • P. pastoris for proteins requiring eukaryotic post-translational modifications

  • S. cerevisiae for proteins sensitive to bacterial expression conditions

Optimization parameters include:

• Induction temperature (typically lower temperatures of 16-25°C improve solubility)

• Induction duration (4-24 hours depending on protein stability)

• Induction agent concentration (0.1-1.0 mM IPTG for E. coli systems)

• Media composition (enriched media such as TB or autoinduction media)

Similar approaches have been successfully used for other bacterial elongation factors and can be adapted for U. urealyticum fusA based on its sequence characteristics and predicted structural properties .

How can site-directed mutagenesis be effectively designed to study functional domains of U. urealyticum Elongation factor G?

Effective site-directed mutagenesis for studying U. urealyticum Elongation factor G functional domains requires:

This approach parallels successful studies of other bacterial proteins, such as the UMP kinase from U. parvum where site-directed mutagenesis (F133N and F133A) was used to investigate the role of specific residues in enzyme function and regulation .

What are the technical challenges in crystallizing U. urealyticum Elongation factor G and how can they be overcome?

Crystallizing U. urealyticum Elongation factor G presents several technical challenges that can be addressed through specific strategies:

• Protein stability issues:

  • Challenge: EF-G can be conformationally flexible, especially in the absence of ligands

  • Solution: Co-crystallization with non-hydrolyzable GTP analogs (GMPPNP, GMPPCP) to stabilize one conformation

  • Solution: Limited proteolysis to identify and remove flexible regions while retaining functional core

• Protein homogeneity:

  • Challenge: Multiple conformational states impeding crystal formation

  • Solution: Size exclusion chromatography as a final purification step to ensure monodispersity

  • Solution: Dynamic light scattering to assess sample homogeneity before crystallization trials

• Crystallization conditions:

  • Challenge: Identifying optimal conditions for crystal nucleation and growth

  • Solution: High-throughput screening with commercially available sparse matrix screens

  • Solution: Microseeding techniques to promote crystal growth

  • Solution: Crystallization with antibody fragments or designed binding proteins to provide crystal contacts

• Data collection and processing:

  • Challenge: Radiation damage during X-ray exposure

  • Solution: Collection at cryogenic temperatures with appropriate cryoprotectants

  • Solution: Multiple crystals may be needed for complete datasets

These approaches have been successful with other bacterial translation factors and could be applied to U. urealyticum EF-G based on the experience with other Ureaplasma proteins like UMP kinase, which was successfully crystallized and its structure determined .

How does the genetic variability of the fusA gene contribute to antimicrobial resistance in Ureaplasma species?

The genetic variability of the fusA gene contributes to antimicrobial resistance in Ureaplasma species through several mechanisms:

• Target site modifications: Specific mutations in the fusA gene can alter the structure of Elongation factor G, reducing its affinity for antimicrobials that target protein synthesis (like tetracyclines and macrolides).

• Cross-resistance patterns: Mutations in fusA may confer resistance to multiple antimicrobial classes, complicating treatment strategies. Studies show varying susceptibility patterns among Ureaplasma isolates to doxycycline (91%), josamycin (86%), ofloxacin (77%), and azithromycin (71%) .

• Serovar-specific variations: Different U. urealyticum serovars may carry naturally occurring polymorphisms in fusA that influence their intrinsic susceptibility to antimicrobials, potentially explaining the observed differences in treatment efficacy.

• Selective pressure effects: Widespread use of protein synthesis inhibitors creates selective pressure for fusA mutations, particularly in clinical settings with high antimicrobial usage.

To investigate these relationships, researchers typically:

• Sequence the fusA gene from clinical isolates with different antimicrobial susceptibility profiles

• Perform correlation analyses between specific mutations and minimum inhibitory concentrations

• Create recombinant strains with site-directed mutations to confirm causative relationships

• Develop rapid molecular tests to identify resistance-associated mutations for clinical guidance

This research supports the development of targeted antimicrobial strategies and helps explain treatment failures in Ureaplasma infections .

What is the potential of Elongation factor G as a diagnostic marker or therapeutic target for Ureaplasma infections?

Elongation factor G shows considerable potential as both a diagnostic marker and therapeutic target for Ureaplasma infections:

Diagnostic Applications:

• Species-specific detection: The high conservation of fusA within U. urealyticum serovars (>99.97%) makes it an excellent target for species-specific diagnostic assays .

• Quantitative assessment: Real-time PCR targeting fusA can provide quantitative data on bacterial load, helping distinguish colonization from infection .

• Antimicrobial resistance prediction: Specific mutations in fusA can serve as molecular markers for predicting resistance to certain antimicrobials.

• Point-of-care testing: Development of isothermal amplification methods targeting fusA could enable rapid diagnostics in clinical settings.

Therapeutic Target Potential:

• Essential function: As a critical factor in protein synthesis, inhibition of EF-G would be lethal to Ureaplasma.

• Structural uniqueness: Despite conservation, structural differences between bacterial and human elongation factors enable selective targeting.

• Novel antimicrobial development: Structure-based drug design can utilize unique features of U. urealyticum EF-G to develop specific inhibitors.

• Combination therapy approaches: EF-G inhibitors could be used alongside existing antimicrobials to enhance efficacy and reduce resistance development.

Implementation of these applications requires further research, including structural characterization, inhibitor screening, and clinical validation studies. The high specificity of fusA-based approaches could address current challenges in Ureaplasma diagnostics and treatment .

How can fusA sequencing data be integrated with other molecular markers for improved epidemiological studies of Ureaplasma infections?

Integration of fusA sequencing data with other molecular markers can significantly enhance epidemiological studies of Ureaplasma infections through:

• Multi-locus sequence typing (MLST) approaches:

  • Combining fusA with other housekeeping genes to create high-resolution strain typing

  • Developing standardized MLST schemes specifically for Ureaplasma species

  • Enabling precise tracking of transmission patterns within communities

• Virulence factor correlation:

  • Associating specific fusA variants with genes encoding virulence factors

  • Creating virulence profiles that correlate with clinical outcomes

  • Identifying high-risk strains requiring more aggressive management

• Resistance determinant mapping:

  • Correlating fusA sequences with other resistance genes (e.g., tet(M) for tetracycline resistance)

  • Developing comprehensive resistance profiles for each strain

  • Tracking the evolution and spread of multi-resistant strains

• Phylogenetic analysis integration:

  • Using fusA alongside other markers like the mba gene for enhanced phylogenetic resolution

  • Creating databases of strain types with associated clinical data

  • Distinguishing between persistent infection and reinfection in longitudinal studies

• Clinical dataset correlation:

  • Linking molecular data with patient demographics and outcomes

  • Identifying population-specific strain distributions

  • Developing predictive models for infection risk and treatment response

Implementation requires:

• Standardized protocols for multi-gene sequencing

• Centralized databases for data deposition and comparison

• Statistical approaches for integrating diverse datasets

• Collaboration between clinical and research laboratories

This integrated approach overcomes limitations of single-gene typing methods previously used for Ureaplasma, which often lacked capacity for complete separation of all 14 serovars .

How do post-translational modifications affect the function of Elongation factor G in Ureaplasma urealyticum?

Post-translational modifications (PTMs) of Elongation factor G in U. urealyticum represent an understudied aspect of its biology with significant functional implications:

• Types of potential PTMs in bacterial EF-G:

  • Phosphorylation of serine, threonine, or tyrosine residues

  • Methylation of lysine or arginine residues

  • ADP-ribosylation by bacterial toxins or endogenous enzymes

  • Acetylation of lysine residues

• Functional consequences of PTMs:

  • Regulation of GTPase activity affecting translation efficiency

  • Altered ribosome binding affinity influencing translation fidelity

  • Changed protein-protein interactions with other translation factors

  • Modified susceptibility to antimicrobial agents targeting protein synthesis

• Methodological approaches to study PTMs:

  • Mass spectrometry-based proteomics to identify and characterize modifications

  • Site-directed mutagenesis of modified residues to assess functional impact

  • In vitro modification assays to determine enzyme specificity

  • Antibodies against specific modifications for detection in native conditions

• U. urealyticum-specific considerations:

  • The minimal genome of Ureaplasma suggests potentially streamlined regulation

  • Limited metabolic capacity may affect the types of modifications possible

  • Unique environmental adaptations may have selected for specific regulatory mechanisms

This research area represents a frontier in understanding translation regulation in minimal genome organisms and may reveal novel mechanisms of antimicrobial resistance and environmental adaptation in U. urealyticum.

What role does Elongation factor G play in the unique adaptations of Ureaplasma to its host environment?

Elongation factor G plays several critical roles in Ureaplasma's adaptation to its host environment:

• Translational efficiency under resource limitation:

  • U. urealyticum has one of the smallest genomes among self-replicating organisms

  • EF-G optimization may allow efficient protein synthesis despite limited resources

  • Potential adaptations in GTP hydrolysis rates to balance speed and accuracy

• Specialized translation under pH fluctuations:

  • Ureaplasma generates energy through urea hydrolysis, creating microenvironmental pH changes

  • EF-G may have evolved structural adaptations for stability across pH ranges

  • Translational regulation in response to pH stress may involve EF-G modifications

• Host-interface translational regulation:

  • Potential role in selective translation of proteins involved in host interaction

  • Possible involvement in stress-responsive translation during host immune challenges

  • May participate in regulatory networks controlling expression of adhesins and immune evasion factors

• Antimicrobial target characteristics:

  • Structural adaptations that maintain function while potentially altering drug binding sites

  • Specialized features that balance the need for conservation of function with evasion of host immune recognition

  • Possible involvement in the distinctive antimicrobial susceptibility profiles of Ureaplasma

Research approaches to investigate these adaptations include:

• Comparative structural analysis with EF-G from free-living bacteria

• Expression studies under conditions mimicking the host environment

• Translational efficiency assays at different pH and nutrient conditions

• Ribosome profiling to identify mRNAs preferentially translated under stress

These studies would help explain the successful adaptation of Ureaplasma to its restricted host niche and potentially identify novel intervention targets .

How can systems biology approaches integrate fusA expression data with metabolic and transcriptomic profiles in Ureaplasma?

Systems biology approaches can effectively integrate fusA expression data with metabolic and transcriptomic profiles in Ureaplasma through:

• Multi-omics data integration frameworks:

  • Correlation of fusA expression with global transcriptome changes under various conditions

  • Integration with metabolomic data to understand how translation regulation affects metabolic flux

  • Proteome-wide analyses to identify co-regulated protein networks

  • Mathematical modeling of the relationships between translation efficiency and metabolic output

• Constraint-based modeling approaches:

  • Development of genome-scale metabolic models incorporating translational constraints

  • Flux balance analysis integrating protein synthesis costs

  • Simulation of growth and survival under varying environmental conditions

  • Prediction of essential genes and pathways under different scenarios

• Network analysis methods:

  • Construction of gene regulatory networks centered on translation factors

  • Identification of regulatory hubs and motifs controlling adaptation

  • Inference of causal relationships between fusA expression and virulence traits

  • Comparison with networks from other minimal genome organisms

• Temporal dynamics studies:

  • Time-series experiments capturing rapid regulatory responses

  • Analysis of translation factor dynamics during host cell interaction

  • Study of adaptation trajectories during antibiotic exposure

  • Modeling of population heterogeneity in expression patterns

Implementation challenges include:

• The limited biomass available from Ureaplasma cultures

• Technical difficulties in synchronizing cells for temporal studies

• Computational challenges in integrating heterogeneous data types

• Need for specialized growth media that may influence natural behavior

These approaches could reveal how this minimal genome pathogen achieves regulatory flexibility despite limited genetic resources and identify potential vulnerabilities for therapeutic targeting .