Recombinant Leptospira interrogans serogroup Icterohaemorrhagiae serovar copenhageni Ribosomal RNA large subunit methyltransferase N (rlmN)

What is the function of Ribosomal RNA large subunit methyltransferase N (rlmN) in Leptospira interrogans?

Based on studies in other bacteria, rlmN catalyzes the formation of N2-methyladenosine (m2A) in both 23S ribosomal RNA and transfer RNA . In bacterial pathogens, rlmN appears to function as a redox-sensitive molecular switch that directly relays oxidative stress signals to modulate translation through modifications of the rRNA and tRNA epitranscriptome . This function may be particularly important for L. interrogans during host infection when the bacteria encounters oxidative stress from host immune responses.

How does rlmN relate to virulence in Leptospira interrogans serovar Copenhageni?

While direct research on rlmN's role in L. interrogans virulence is limited, studies in other pathogens suggest rlmN may contribute to virulence by helping bacteria survive oxidative stress. In Enterococcus faecalis, genetic knockout of rlmN produces a proteome that mimics the oxidative stress response, with increased levels of superoxide dismutase and decreased virulence proteins . In L. interrogans, which must survive both oxidative stress in mammalian hosts and diverse environmental conditions, rlmN likely plays a role in adaptation and survival during infection, particularly since L. interrogans is an obligate aerobe that must avoid reactive oxygen species during metabolism .

What is the genomic context of rlmN in L. interrogans serovar Copenhageni?

L. interrogans serovar Copenhageni has a genome consisting of two circular chromosomes with a total of almost 4.7 Mbp . The larger chromosome is 4.3 Mbp and the smaller chromosome is 350 Kbp, with a G+C content of 35% containing 3,400-3,700 protein-coding genes . The rlmN gene should be located within this genomic context, though its precise location is not specified in the provided literature. Unlike many other bacteria where rRNA genes are clustered, in Leptospira the 16S, 23S, and 5S rRNA genes are scattered on the large chromosome , which may have implications for the regulation and function of rlmN.

What are the optimal conditions for expressing recombinant L. interrogans rlmN protein?

For optimal expression of functional recombinant rlmN from L. interrogans, researchers should consider:

• Expression system: Baculovirus expression systems have been successfully used for related methyltransferases .

• Temperature and induction conditions: Lower temperatures (16-20°C) during induction may increase soluble protein yield for complex enzymes like rlmN.

• Fusion tags: Consider N-terminal tags such as glutathione-S-transferase (GST) or His-tags to facilitate purification while preserving enzyme activity .

• Cofactor supplementation: Since rlmN contains an Fe-S cluster, expression media may need supplementation with iron sources and reducing agents.

• Anaerobic purification: To maintain integrity of the Fe-S cluster, consider using anaerobic conditions during purification.

This approach takes into account rlmN's role as an RNA-modifying enzyme containing a redox-sensitive Fe-S cluster .

How can I measure rlmN enzymatic activity in L. interrogans?

A comprehensive approach to measuring rlmN activity includes:

In vitro activity assay:

• Prepare purified recombinant rlmN as described above

• Use S-adenosyl-L-methionine (SAM) as methyl donor (see enzyme catalog EC 2.1.1.X)

• Isolate ribosomal RNA substrate from L. interrogans or prepare synthetic RNA fragments containing target sequence

• Incubate enzyme with substrate under appropriate conditions (buffer, pH 7.5-8.0, magnesium ions)

• Detect m2A formation using:

  • HPLC-MS/MS analysis

  • Immunoblotting with antibodies specific for m2A

  • 32P-labeled SAM to track methyl transfer

In vivo activity measurement:

• Generate rlmN knockout strains of L. interrogans

• Compare methylation patterns of rRNA between wild-type and knockout strains

• Analyze changes in proteome composition under normal and oxidative stress conditions

• Quantify m2A levels in total RNA or specific rRNA regions using LC-MS/MS

What approaches are effective for studying the role of rlmN in L. interrogans under oxidative stress?

To study rlmN's role during oxidative stress in L. interrogans:

• Gene expression analysis comparing rlmN expression levels before and after exposure to various oxidative stressors (H₂O₂, superoxide, host immune cells)

• Create an rlmN knockout strain using homologous recombination techniques

• Expose wild-type and rlmN-deficient strains to different oxidative stressors and compare:

  • Survival rates

  • Changes in the tRNA and rRNA epitranscriptome

  • Alterations in protein synthesis patterns

  • Global proteomic changes

• Complementation studies with wild-type and mutant rlmN to confirm phenotype specificity

• Assess the importance of the Fe-S cluster through site-directed mutagenesis of conserved cysteine residues

This approach is supported by findings that ROS-mediated inactivation of the Fe-S cluster-containing methyltransferase rlmN leads to decreases in N2-methyladenosine in both 23S rRNA and tRNA .

How does the structure and function of rlmN differ between serovars Copenhageni and Icterohaemorrhagiae?

While both serovars are closely related with limited genetic differences (1,072 SNPs and 258 indels identified across their genomes ), differences in methyltransferase activity could be significant:

• Genomic analysis revealed that serovars Copenhageni and Icterohaemorrhagiae differ primarily in a frameshift mutation within a homopolymeric tract of the lic12008 gene related to LPS biosynthesis . This mutation might indirectly affect rlmN function through alterations in membrane structure or stress responses.

• Phylogenetic analysis shows that despite being genetically similar, these serovars display distinct spatial clustering . This could result in different selective pressures on RNA modification systems like rlmN.

• rlmN activity may vary between serovars in response to different oxidative stress thresholds, potentially contributing to their different virulence characteristics. Serogroup Icterohaemorrhagiae strains are considered the most virulent of L. interrogans .

• Experimental approach: Compare rlmN sequence, expression levels, and methylation activity between the serovars under standard conditions and oxidative stress. Quantify m2A levels in rRNA and tRNA across both serovars using LC-MS/MS or antibody-based detection methods.

What is the role of rlmN in L. interrogans adaptability to different host environments?

L. interrogans must adapt to diverse environments, from water and soil to mammalian hosts. rlmN likely contributes to this adaptability through:

• Regulation of translation in response to host-induced stress: rlmN modifies rRNA and tRNA, potentially altering translation efficiency of specific transcripts under stress conditions.

• Contribution to immune evasion: Modifications in ribosomal RNA may affect production of immunogenic proteins. L. interrogans has unique LPS with a methylated phosphate residue not recognized by human TLR4 , suggesting specialized methylation systems are important for immune evasion.

• Adaptation to host oxidative defenses: As an obligate aerobe, L. interrogans must manage reactive oxygen species during metabolism . The perRA and perRB genes encode peroxide-responsive regulators , which may interact with rlmN function.

• Support for differential gene expression during infection phases: The biphasic infection pattern of L. interrogans (anicteric and icteric phases) may require dynamic regulation of translation via rlmN-mediated RNA modifications.

Experimental approach: Compare wild-type and rlmN-deficient strains in their ability to survive in different host tissues (kidney, liver) and environmental conditions. Analyze the translatome under different conditions to identify transcripts differentially translated dependent on rlmN activity.

How does oxidative stress affect rlmN activity in L. interrogans during different phases of infection?

During L. interrogans infection, rlmN activity likely responds dynamically to changing oxidative environments:

• Initial infection stage: When L. interrogans first encounters host defenses, rlmN may maintain activity to ensure proper translation of proteins needed for invasion and initial survival.

• Acute/spirochetemic phase: As the bacteria disseminate through the bloodstream, they encounter increased oxidative stress. Studies show L. interrogans can be found in various tissues including testes during this phase . During this period, rlmN activity may be compromised by ROS, leading to decreased m2A modifications in rRNA/tRNA.

• Chronic/colonization phase: During kidney colonization, oxidative stress may be lower, potentially allowing recovery of rlmN activity and restoration of normal translation patterns.

Experimental approach: Isolate L. interrogans from different tissues and at different timepoints during infection. Measure:

• rlmN protein levels and activity

• m2A modification levels in rRNA and tRNA

• Global translation patterns

• Oxidative stress markers

What bioinformatic approaches can identify potential functional domains in L. interrogans rlmN?

For comprehensive bioinformatic analysis of L. interrogans rlmN:

• Sequence alignment with known rlmN proteins from related species using tools like MUSCLE or CLUSTALW to identify:

  • Conserved domains for SAM binding

  • Fe-S cluster coordination sites

  • RNA substrate recognition motifs

• Homology modeling using available crystal structures of related methyltransferases as templates (SWISS-MODEL, Phyre2)

• Molecular dynamics simulations to predict:

  • Conformational changes during substrate binding

  • Effects of oxidative stress on protein structure

  • Electron transfer pathways involving the Fe-S cluster

• Analysis of genomic context to identify potential regulatory elements and co-transcribed genes

• Taxonomic profiling to track rlmN evolution across Leptospira species, potentially revealing adaptations to different ecological niches

L. interrogans has a complex set of genes associated with chemotaxis and signaling , suggesting sophisticated regulatory networks that may influence rlmN function.

How can RNA-seq data be analyzed to understand the impact of rlmN on the L. interrogans transcriptome?

A systematic approach to analyzing RNA-seq data for understanding rlmN's impact includes:

• Differential expression analysis:

  • Compare transcriptomes of wild-type and rlmN-deficient strains

  • Identify gene sets affected by rlmN deletion under normal and stress conditions

  • Use tools like DESeq2 or EdgeR for statistical analysis

• Ribosome profiling to directly assess translation efficiency changes:

  • Sequence ribosome-protected fragments

  • Calculate translation efficiency for each transcript

  • Identify transcripts with altered translation dependent on rlmN activity

• Epitranscriptome analysis:

  • Use antibody-based enrichment or direct RNA sequencing to map m2A modifications

  • Compare modification patterns between wild-type and rlmN-deficient strains

  • Correlate modifications with translation efficiency

• Pathway enrichment analysis:

  • Identify cellular processes most affected by rlmN deficiency

  • Focus on pathways related to virulence, stress response, and adaptation

• Integration with proteomic data:

  • Correlate transcriptomic changes with alterations in protein abundance

  • Identify post-transcriptional regulation dependent on rlmN

This approach would build on observations that rlmN-mediated RNA modifications can directly regulate translation efficiency and protein expression in response to environmental changes .

What statistical methods are most appropriate for analyzing differences in tRNA modification patterns related to rlmN activity?

For rigorous statistical analysis of tRNA modification patterns:

Data Collection Methods:

• LC-MS/MS quantification of modified nucleosides

• Next-generation sequencing with modification-sensitive protocols

• Nanopore direct RNA sequencing for single-molecule modification detection

Statistical Analysis Approaches:

• Paired sample tests (t-test or Wilcoxon) for comparing modification levels between:

  • Wild-type vs. rlmN knockout strains

  • Normal vs. oxidative stress conditions

  • Different tRNA species or modification positions

• Multi-factor ANOVA to assess interactions between:

  • Growth conditions

  • tRNA species

  • Genetic background

• Bayesian hierarchical modeling to account for:

  • Technical variation in modification detection

  • Biological variability

  • Interdependence of modifications

• Machine learning approaches:

  • Random forests or support vector machines to identify modification patterns

  • Neural networks to predict functional consequences of modification changes

Visualization Methods:

• Heatmaps of modification levels across tRNA species

• Principal component analysis plots

• Modification level changes mapped to tRNA secondary structures

This statistical framework would help distinguish biologically significant changes from technical variation, particularly important given the subtle effects that individual RNA modifications may have on bacterial physiology.

How might L. interrogans rlmN function as a target for novel antimicrobial strategies?

The potential of rlmN as an antimicrobial target stems from several factors:

• Essential role in pathogen survival: If rlmN is required for L. interrogans adaptation to host environments, inhibiting it could reduce bacterial fitness during infection.

• Unique properties compared to host enzymes: The Fe-S cluster dependency of bacterial rlmN may allow development of inhibitors with minimal effects on host cellular processes.

• Potential combination therapy approaches:

  • rlmN inhibitors could sensitize L. interrogans to oxidative stress

  • Combining with antibiotics that induce oxidative stress could increase efficacy

  • Targeting multiple RNA modification enzymes simultaneously could prevent compensatory mechanisms

• Potential drug development strategies:

  • High-throughput screening for inhibitors of purified rlmN

  • Structure-based drug design targeting the SAM binding pocket

  • Allosteric inhibitors affecting Fe-S cluster stability

  • Peptidomimetics blocking RNA substrate binding

• Considerations:

  • Target validation through genetic and biochemical approaches

  • Assessment of inhibitor specificity across bacterial species

  • Evaluation of resistance development potential

Experimental approach: Screen chemical libraries against purified rlmN, validate hits in culture, and test efficacy in animal models of leptospirosis.

What role might rlmN play in the development of diagnostic tools or vaccines for leptospirosis?

Leveraging rlmN in diagnostic and vaccine development:

Diagnostic Applications:

• Antibody detection: If rlmN is immunogenic during natural infection, anti-rlmN antibodies could serve as diagnostic markers.

• Nucleic acid detection: rlmN sequence conservation across pathogenic Leptospira could enable PCR-based diagnostics.

• Antigen detection: If rlmN is secreted or released during infection, it could be detected in patient samples.

Vaccine Development Potential:

• Recombinant rlmN as vaccine antigen:

  • If surface-exposed or secreted during infection

  • If immunogenic and capable of eliciting protective responses

• Attenuated vaccine strains:

  • rlmN-modified strains with reduced virulence but retained immunogenicity

  • Similar to how recombinant LigA from L. interrogans showed protection in hamster models

• Epitope mapping:

  • Identification of immunogenic epitopes within rlmN

  • Design of epitope-based vaccines incorporating multiple leptospiral antigens

Current leptospirosis vaccines have limitations including "suboptimal protection, need for frequent booster doses, and specificity to certain serovars" . A molecular understanding of rlmN could contribute to more effective, broadly protective vaccine designs.

How can systems biology approaches integrate rlmN function into larger models of L. interrogans pathogenesis?

Systems biology offers powerful approaches to contextualize rlmN function:

• Multi-omics integration:

  • Combine transcriptomics, proteomics, and metabolomics data

  • Compare wild-type and rlmN-deficient strains under various conditions

  • Map changes in RNA modifications to alterations in protein expression and metabolite levels

• Network analysis:

  • Construct gene regulatory networks incorporating rlmN

  • Identify hub genes and pathways affected by rlmN activity

  • Model effects of oxidative stress on these networks

• Host-pathogen interaction modeling:

  • Simulate rlmN's role during different infection stages

  • Model effects of host immune responses on rlmN function

  • Predict consequences of rlmN inhibition during infection

• Experimental validation approaches:

  • Targeted metabolomics to track changes in specific pathways

  • ChIP-seq to identify transcription factors affected by rlmN activity

  • CRISPR interference screens to identify genetic interactions with rlmN

This approach builds on evidence that oxidative stress triggers widespread changes in bacterial physiology , with rlmN potentially serving as a key regulatory node connecting environmental sensing to adaptive responses.