Recombinant Enterococcus faecalis NAD (+)--arginine ADP-ribosyltransferase EFV (EF_0335)

What is the function of NAD(+)-arginine ADP-ribosyltransferase EFV in Enterococcus faecalis?

NAD(+)-arginine ADP-ribosyltransferase EFV (EF_0335) is an enzyme involved in post-translational modifications that plays a role in bacterial stress responses. Similar to other RNA modification enzymes in E. faecalis, it likely participates in regulatory functions related to translation and protein synthesis during stress conditions. E. faecalis possesses elaborate systems to manage reactive oxygen and nitrogen species (ROS) arising from exposure to the mammalian immune system and environmental stresses, and enzymes like EFV contribute to these stress response mechanisms .

How does EFV relate to other stress response proteins in E. faecalis?

EFV functions within a network of stress response proteins in E. faecalis. Research has shown that E. faecalis responds to oxidative stress through various mechanisms, including RNA modification enzymes that regulate translation. For example, the RNA methyltransferase RlmN has been identified as a redox-sensitive molecular switch that directly relays oxidative stress to modulate translation through epitranscriptomic changes in rRNA and tRNA. Similar to EFV, these stress response systems help the bacterium adapt to environmental challenges such as antibiotic exposure and oxidative stress .

What experimental models are suitable for studying EFV?

Suitable experimental models for studying EFV include both in vitro biochemical assays and in vivo approaches using E. faecalis strains such as OG1RF (derived from human commensal oral isolate OG1) and V583 (a multidrug-resistant clinical isolate). These models allow researchers to investigate enzyme activity, substrate specificity, and physiological roles. When studying stress responses, researchers often expose bacteria to sublethal doses of stress-inducing agents like antibiotics or reactive oxygen species generators (e.g., menadione) to observe the resulting changes in protein expression and enzyme activity .

How does oxidative stress affect EFV activity and what are the implications for bacterial stress response?

Oxidative stress likely affects EFV activity similarly to other Fe-S cluster-containing enzymes in E. faecalis. Research on related enzymes has shown that reactive oxygen species (ROS) can disrupt Fe-S clusters, leading to inactivation of enzymes like RlmN. This inactivation serves as a sensing mechanism that links environmental stressors to changes in the bacterial proteome. When studying EFV, researchers should consider how oxidative stress might alter its activity and subsequently affect downstream stress response pathways. Experimental approaches could include exposing recombinant EFV to various oxidative agents and measuring changes in enzymatic activity .

What is the relationship between EFV activity and antibiotic resistance mechanisms?

The relationship between EFV activity and antibiotic resistance likely involves complex regulatory networks similar to those observed with other E. faecalis enzymes. Studies with RlmN have shown that loss of enzyme activity can confer resistance to certain antibiotics, such as a 16-fold increase in MIC for chloramphenicol. When investigating EFV, researchers should examine how its activity changes in response to different antibiotics and whether these changes contribute to resistance mechanisms. This can be studied by creating knockout mutants (ΔEFV) and comparing their antibiotic susceptibility profiles to wild-type strains .

How does the epitranscriptomic activity of EFV compare with other RNA-modifying enzymes in E. faecalis?

To compare EFV's epitranscriptomic activity with other RNA-modifying enzymes, researchers should conduct comprehensive analyses of modified ribonucleosides before and after EFV activity. Similar studies with RlmN involved quantifying 24 modifications in rRNA and tRNA. The analysis revealed that exposure to sublethal doses of ROS-inducing antibiotics led to large decreases in N2-methyladenosine (m2A) in both 23S ribosomal RNA and transfer RNA. Researchers should determine if EFV catalyzes similar or different modifications and how these modifications influence bacterial physiology under various stress conditions .

What are the optimal conditions for expressing and purifying recombinant EFV?

For optimal expression and purification of recombinant EFV, researchers should consider:

• Expression system: E. coli BL21(DE3) or similar expression strains are recommended for recombinant expression.

• Vector selection: pET-based vectors with appropriate tags (His-tag or GST-tag) facilitate purification.

• Induction conditions: IPTG concentration (typically 0.1-1.0 mM), temperature (16-37°C), and duration (3-16 hours) should be optimized.

• Buffer composition: Since EFV likely contains sensitive structural elements similar to the Fe-S clusters in RlmN, buffers should include reducing agents (DTT or β-mercaptoethanol) and potentially oxygen-scavenging systems to prevent oxidative damage during purification.

• Storage conditions: Enzyme stability should be tested at various temperatures (-80°C, -20°C, 4°C) with appropriate cryoprotectants (glycerol, 10-50%).

Researchers should validate protein activity after each purification step to ensure the enzyme remains functional .

In vitro assays:

• Enzymatic activity can be measured using radioisotope-labeled NAD+ to track the transfer of ADP-ribose to arginine residues.

• Substrate specificity can be determined using various potential target proteins or peptides containing arginine residues.

• Kinetic parameters (Km, Vmax) should be established under different conditions (pH, temperature, ionic strength).

In vivo approaches:

• Create knockout mutants (ΔEFV) and analyze phenotypic changes.

• Use targeted proteomics to measure EFV protein levels under various conditions.

• Employ epitranscriptomic analysis methods similar to those used for RlmN studies, which involved quantifying modified ribonucleosides using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

These approaches can help elucidate both the biochemical properties and physiological roles of EFV .

What techniques are most effective for studying EFV's role in antibiotic resistance?

To study EFV's role in antibiotic resistance, researchers should employ the following techniques:

• Minimum Inhibitory Concentration (MIC) determination:

  • Compare MIC values between wild-type and ΔEFV mutants for various antibiotics

  • Test at sublethal concentrations (10-25% of MIC) to observe subtle effects

• Time-kill assays:

  • Exposure of wild-type and ΔEFV mutants to bactericidal antibiotics

  • Quantification of surviving bacteria over time

• Genetic complementation:

  • Create an over-expression strain (similar to RlmNp strains for RlmN studies)

  • Compare antibiotic sensitivity between normal expression and over-expression strains

• Molecular characterization:

  • Analyze changes in the proteome using LC-MS/MS

  • Identify alterations in stress response proteins (e.g., superoxide dismutase) and virulence factors

This multi-faceted approach can reveal how EFV influences antibiotic susceptibility and whether it acts as a molecular switch similar to RlmN .

How should researchers design experiments to investigate EFV's response to oxidative stress?

When designing experiments to investigate EFV's response to oxidative stress, researchers should:

• Select appropriate oxidative stress inducers:

  • Menadione (superoxide generator)

  • Hydrogen peroxide (direct oxidant)

  • Antibiotics known to induce ROS (erythromycin, chloramphenicol)

• Establish dose-response relationships:

  • Use concentration ranges from 10-25% of MIC for antibiotics

  • Determine sublethal doses that induce stress without causing significant cell death

• Include relevant controls:

  • Wild-type E. faecalis strains (OG1RF, V583)

  • ΔEFV knockout mutants

  • Strains with over-expressed EFV

• Measure multiple parameters:

  • EFV activity

  • EFV protein levels (targeted proteomics)

  • EFV mRNA levels (qPCR)

  • Global proteome changes

  • Epitranscriptome modifications

This comprehensive approach can reveal whether EFV acts as a redox sensor similar to RlmN, which has been shown to serve as a molecular switch relaying oxidative stress to translational regulation .

What are the key parameters for measuring EFV sensor performance in research applications?

When evaluating EFV sensor performance in research applications, researchers should consider:

• Sensitivity parameters:

  • Minimum detectable response

  • Linear range of detection

  • Response time to stimulus

• Specificity considerations:

  • Cross-reactivity with related compounds

  • Interference from biological matrices

  • Selectivity for target analytes

• Environmental variables:

  • Temperature effects (calibration at multiple temperatures, e.g., 23°C reference)

  • Barometric pressure influences

  • Flow rate dependencies

• Calibration methodology:

  • Multi-point calibration curves

  • Functional relationships between output and analyte concentration

  • Mathematical models for response prediction

These parameters should be systematically evaluated using calibrated reference instruments and standardized testing conditions to ensure reliable and reproducible results .

How should researchers analyze epitranscriptomic data in the context of EFV activity?

Analyzing epitranscriptomic data in the context of EFV activity requires a structured approach:

• Comprehensive modification profiling:

  • Quantify at least 24 modified ribonucleosides in both rRNA and tRNA

  • Use LC-MS/MS with appropriate internal standards for accurate quantification

  • Compare profiles before and after exposure to stressors

• Statistical analysis:

  • Apply appropriate statistical tests (t-tests, ANOVA) to identify significant changes

  • Control for multiple hypothesis testing using methods like Benjamini-Hochberg correction

  • Consider biological replicates (n≥3) to ensure reproducibility

• Correlation analysis:

  • Relate changes in specific modifications to EFV activity levels

  • Compare wild-type and ΔEFV knockout strains to identify EFV-dependent modifications

  • Correlate epitranscriptomic changes with phenotypic outcomes

• Pathway integration:

  • Map modifications to specific RNA positions and their known functions

  • Analyze codon context for tRNA modifications

  • Integrate with proteomics data to establish modification-translation relationships

This approach has revealed important insights for other RNA-modifying enzymes like RlmN, which shows selective reduction of m2A in response to specific antibiotics .

What are the common sources of experimental error when working with EFV, and how can they be mitigated?

Common sources of experimental error when working with EFV and mitigation strategies include:

By systematically addressing these potential sources of error, researchers can improve the reliability and reproducibility of their experiments with EFV .