Recombinant Lactobacillus johnsonii Uncharacterized RNA methyltransferase LJ_1698 (LJ_1698)

What bioinformatic approaches can best predict the function of uncharacterized RNA methyltransferase LJ_1698?

Predicting the function of uncharacterized RNA methyltransferases like LJ_1698 requires a comprehensive bioinformatic pipeline combining several approaches:

• Sequence homology analysis: Conduct BLASTp searches against characterized methyltransferases in related species. Compare LJ_1698 with known methyltransferases like METTL16, which has been shown to methylate the conserved ACAGAGA sequence in U6 snRNA at position A43 .

• Domain architecture analysis: Identify conserved methyltransferase domains such as S-adenosylmethionine (SAM) binding motifs, which are characteristic of RNA methyltransferases. Tools like InterProScan, Pfam, and SMART can reveal these functional domains.

• Structural prediction: Use AlphaFold2 or RoseTTAFold to generate 3D structural models, then compare with crystallized methyltransferases using tools like Dali or TM-align to identify structural similarities regardless of sequence conservation.

• Phylogenetic analysis: Construct phylogenetic trees including characterized bacterial methyltransferases to place LJ_1698 in evolutionary context, potentially revealing functional relationships.

• Genomic context analysis: Examine neighboring genes in the L. johnsonii genome since functionally related genes are often clustered together in bacterial genomes.

What expression systems are most efficient for producing recombinant L. johnsonii RNA methyltransferases for biochemical characterization?

Selection of appropriate expression systems for LJ_1698 requires consideration of several factors:

• E. coli expression systems: The BL21(DE3) strain with pET vector systems typically provides high yields for bacterial proteins. Consider using codon-optimized constructs for L. johnsonii genes due to potential codon usage bias. The C41(DE3) and C43(DE3) strains are recommended for potentially toxic proteins.

• Expression tags selection: For RNA methyltransferases, N-terminal His6 tags are preferable as C-terminal tags might interfere with RNA substrate recognition. Include a TEV protease cleavage site for tag removal if needed for activity assays.

• Induction conditions optimization: Test multiple conditions by varying IPTG concentration (0.1-1.0 mM), temperature (16-37°C), and induction duration (4-24 hours) to maximize soluble protein yield.

• Alternative expression hosts: If E. coli expression fails, consider Lactobacillus-based expression systems, which may better maintain proper folding of L. johnsonii proteins. The taxonomic identification methods used for L. johnsonii strains can guide the development of species-specific expression systems .

• Solubility enhancement: Co-express with chaperones (GroEL/GroES, DnaK/DnaJ) or use fusion tags (MBP, SUMO) to improve solubility of LJ_1698.

What experimental methods are most suitable for characterizing the RNA substrate specificity of LJ_1698?

To determine RNA substrates of LJ_1698, a multi-method approach is recommended:

• RNA immunoprecipitation (RIP): Using antibodies against tagged LJ_1698, precipitate the enzyme along with its bound RNA substrates. This approach was successfully used to identify RNA targets of methyltransferases like METTL16 .

• Crosslinking and Analysis of cDNA (CRAC): This method reveals direct RNA-protein interactions by UV crosslinking followed by immunoprecipitation and high-throughput sequencing. For LJ_1698, adapting the CRAC protocol that identified METTL16 binding to U6 snRNA and other RNAs would be valuable .

• In vitro methylation assays: Purify LJ_1698 and incubate with candidate RNA substrates in the presence of radiolabeled SAM. After digestion, analyze methylated nucleotides by thin-layer chromatography or HPLC.

• Methylated RNA Immunoprecipitation (MeRIP-seq): Use antibodies against specific methylation marks (e.g., m6A) to precipitate methylated RNAs from wild-type and LJ_1698 knockout strains, followed by sequencing to identify differentially methylated transcripts.

• SCARLET (Site-specific Cleavage And Radioactive-labeling followed by Ligation-assisted Extraction and Thin-layer chromatography): This technique can precisely identify the methylation position within an RNA substrate.

How can phenotypic screening be optimized to identify the biological functions of LJ_1698 in L. johnsonii?

Phenotypic screening to elucidate LJ_1698 function should include:

• Generation of gene knockout strains: Create precise LJ_1698 deletion mutants using CRISPR-Cas9 or homologous recombination methods. Complement with wild-type LJ_1698 to confirm phenotypes are due to the deletion.

• Stress response assays: Test the knockout strain under various stressors including:

  • Acidic and bile salt conditions to mimic GI tract environments (pH 2.5, 0.3% bovine bile salt)

  • Heat stress conditions (similar to studies showing L. johnsonii's role in heat stress resistance)

  • Oxidative stress (H₂O₂ exposure)

  • Antibiotic challenges to assess potential involvement in resistance mechanisms

• Growth curves analysis: Monitor growth parameters under various conditions, comparing wild-type and knockout strains.

• Host-microbe interaction models: Test the ability of knockout strains to colonize and persist in relevant animal models, examining:

  • Immune response modulation (cytokine profiles, dendritic cell function)

  • Antagonistic activity against pathogens like Salmonella

  • Respiratory protection capabilities

• Transcriptome analysis: Compare the transcriptome profiles of wild-type and knockout strains to identify pathways affected by LJ_1698 deletion.

What analytical techniques can accurately identify and quantify RNA modifications introduced by LJ_1698?

Detection and quantification of RNA modifications require specialized techniques:

• LC-MS/MS analysis: The gold standard for identifying RNA modifications, providing both positional information and modification type. For uncharacterized methyltransferases like LJ_1698, compare methylation patterns in total RNA from wild-type and knockout strains.

• MazF-based approaches: Use the MazF endoribonuclease, which cleaves RNA at ACA sequences unless the A is methylated, to map m6A sites potentially introduced by LJ_1698.

• Nanopore direct RNA sequencing: This technique can detect RNA modifications as they cause characteristic disruptions in current signals during sequencing. It offers the advantage of analyzing native RNA without amplification bias.

• DART-seq (Deamination Adjacent to RNA Modification Targets): Utilizes the differential reactivity of modified vs. unmodified nucleotides to chemical treatments to identify modification sites.

• Antibody-based detection: For specific modifications like m6A, use antibodies for dot blots, Northern blots after immunoprecipitation (as shown with U6 snRNA) , or immunofluorescence microscopy to localize modified RNAs.

How can researchers determine if LJ_1698 targets specific RNA structural motifs rather than sequence motifs?

Investigating structural motif recognition by LJ_1698 requires sophisticated approaches:

• SHAPE-MaP (Selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling): This technique reveals RNA secondary structure by chemical probing. Compare SHAPE reactivity patterns of target RNAs with and without LJ_1698 to identify structural changes at binding sites.

• Structure-based mutagenesis: Systematically introduce mutations that alter RNA structure while minimally changing sequence, then assess methylation efficiency. This approach helped characterize METTL16, which targets highly structured lncRNAs and ncRNAs .

• RNA Bind-n-Seq with structural variants: Generate a library of random RNA sequences, fold them in vitro, and identify which structures are preferentially bound by LJ_1698.

• Cryo-EM or X-ray crystallography: Obtaining structures of LJ_1698 in complex with substrate RNAs can directly reveal structural recognition elements, similar to structural studies that revealed how METTL16 interacts with U6 snRNA .

• CLIP-seq with structure prediction: Combine CLIP-seq data with in silico RNA structure prediction to identify common structural features in LJ_1698 binding sites.

• In silico docking and molecular dynamics: Use computational models to predict and simulate the interaction between LJ_1698 and various RNA structural motifs, guided by experimental binding data.

What experimental approaches can establish if LJ_1698 activity contributes to L. johnsonii stress response and adaptation mechanisms?

To connect LJ_1698 activity with stress adaptation:

• Ribosome profiling under stress conditions: Compare translational efficiency between wild-type and LJ_1698 knockout strains under various stressors (acid, bile, heat) to identify translationally regulated stress response genes. Implement modified ribosome profiling protocols used for L. johnsonii strains N5 and N7, which showed high survival rates in acidic and bile environments .

• Temporal transcriptomics and epitranscriptomics: Track changes in gene expression and RNA modification patterns during stress exposure, mapping the temporal relationship between LJ_1698 activity and stress response activation.

• Competitive fitness assays: Co-culture wild-type and LJ_1698 knockout strains under various stress conditions and track population dynamics to quantify fitness differences.

• Targeted metabolomics: Measure changes in metabolites associated with stress response (e.g., compatible solutes, stress-induced signaling molecules) in the presence and absence of LJ_1698.

• RNA stability measurements: Determine if LJ_1698-mediated modifications affect RNA stability under stress conditions using RNA-seq after transcription inhibition.

• Heterologous expression complementation: Express LJ_1698 in other bacterial species and assess if it confers enhanced stress tolerance, particularly focusing on acidic and bile salt tolerance mechanisms documented in L. johnsonii strains .

How might LJ_1698 influence the immunomodulatory properties of L. johnsonii in host-microbe interactions?

Investigating LJ_1698's role in immunomodulation requires:

• Dendritic cell (DC) stimulation assays: Compare the ability of wild-type and LJ_1698 knockout L. johnsonii to modulate DC function, measuring changes in cytokine production and surface marker expression. This builds on existing knowledge that L. johnsonii plays a crucial role in regulating DC function .

• T-cell polarization experiments: Co-culture T cells with DCs previously exposed to wild-type or knockout L. johnsonii, then measure T-cell subset differentiation and cytokine production.

• Macrophage response profiling: Evaluate macrophage activation status after exposure to wild-type vs. knockout strains, looking for differences in phagocytic activity, respiratory burst, and cytokine secretion patterns, since L. johnsonii is known to alter macrophage function .

• In vivo immunomodulation models: Using appropriate animal models (e.g., colitis, allergy, or respiratory infection models), compare the immunomodulatory effects of wild-type and LJ_1698 knockout strains.

• Transcriptional analysis of host cells: Perform RNA-seq on host immune cells after interaction with wild-type or knockout L. johnsonii to identify differentially regulated immune pathways.

• Respiratory syncytial virus (RSV) protection model: Test if LJ_1698 contributes to the previously documented ability of L. johnsonii to reduce RSV-induced pulmonary responses through immunomodulatory metabolites .

What methodological approaches can determine if LJ_1698 influences RNA splicing in L. johnsonii similar to methyltransferases in eukaryotes?

While bacteria lack classic spliceosomal machinery, RNA processing is still important. To investigate if LJ_1698 affects RNA processing:

• RNA-seq with splice junction analysis: Compare RNA processing patterns between wild-type and LJ_1698 knockout strains, looking for differences in operon expression, RNA cleavage patterns, or alternative processing events.

• RNA structural probing of potential targets: Since methylation can alter RNA structure (as seen with m6A43 in U6 snRNA affecting base pairing with pre-mRNAs) , examine if LJ_1698-mediated modifications alter the structure of bacterial RNAs.

• Nascent RNA capture: Use metabolic labeling to capture newly synthesized RNA and track processing events in the presence and absence of LJ_1698.

• Protein-RNA interaction mapping: Identify proteins that differentially interact with methylated vs. unmethylated RNA to understand downstream effects of LJ_1698 activity.

• 3'- and 5'-RACE: Map precise transcript ends in wild-type and knockout strains to detect changes in RNA processing.

• Heterologous expression in eukaryotic systems: Express LJ_1698 in yeast or mammalian cells and assess if it can methylate eukaryotic RNAs or affect splicing, similar to studies of METTL16's effects on splicing regulation .

How can researchers integrate methylome, transcriptome, and proteome data to build a comprehensive model of LJ_1698 function in L. johnsonii biology?

Multi-omics integration requires sophisticated analytical approaches:

• Coordinated multi-omics data collection: Generate datasets from the same biological samples under identical conditions for:

  • Methylome analysis (MeRIP-seq or similar)

  • Transcriptome analysis (RNA-seq)

  • Proteome analysis (LC-MS/MS)

  • Ribosome profiling (Ribo-seq)

• Network analysis: Construct regulatory networks connecting methylation patterns to transcriptional and translational changes, using tools like WGCNA or Bayesian network inference.

• Machine learning approaches: Train models to predict the functional consequences of LJ_1698-mediated RNA methylation based on integrated omics data.

• Causal inference testing: Use statistical approaches to determine directionality of effects between methylation and downstream processes.

• Time-course experiments: Collect multi-omics data after LJ_1698 induction or inhibition to establish temporal relationships between methylation events and subsequent molecular changes.

• Comparative analysis with other L. johnsonii strains: Integrate genomic data from multiple strains (like the 149 L. johnsonii strains analyzed in NCBI databases) to identify correlations between LJ_1698 sequence variations and phenotypic differences.

What approaches can determine if LJ_1698 influences antibiotic resistance mechanisms in L. johnsonii?

Investigating connections between RNA methylation and antibiotic resistance requires:

• Minimum inhibitory concentration (MIC) testing: Compare antibiotic susceptibility profiles of wild-type and LJ_1698 knockout strains against multiple antibiotic classes, with particular attention to antibiotics targeting RNA processes.

• Antibiotic resistance gene expression analysis: Measure expression of known antibiotic resistance genes (ARGs) like tet(W/N/W), which are widely distributed in L. johnsonii , in the presence and absence of LJ_1698.

• Antibiotic resistance transfer experiments: Assess if LJ_1698 affects the frequency of horizontal gene transfer of antibiotic resistance determinants, particularly since antibiotic resistance in L. johnsonii requires further characterization .

• Ribosome structure and function analysis: Determine if LJ_1698-mediated RNA modifications affect ribosome assembly, structure, or function, potentially influencing susceptibility to antibiotics targeting protein synthesis.

• Competition assays under antibiotic stress: Co-culture wild-type and knockout strains under sub-inhibitory antibiotic concentrations to quantify fitness differences.

• Transcriptomic response to antibiotics: Compare gene expression changes in response to antibiotic exposure between wild-type and knockout strains.

• Biofilm formation analysis: Assess if LJ_1698 affects biofilm formation capacity, which can contribute to antibiotic tolerance.

How can comparative genomics approaches identify evolutionarily conserved functions of LJ_1698 homologs across Lactobacillus species?

To unravel the evolutionary significance of LJ_1698:

• Comprehensive homology search: Identify LJ_1698 homologs across Lactobacillus and related genera using sensitive sequence comparison methods (PSI-BLAST, HMMer).

• Phylogenetic profiling: Correlate the presence/absence of LJ_1698 homologs with specific phenotypic traits across species.

• Selective pressure analysis: Calculate dN/dS ratios to identify regions under purifying or positive selection, indicating functional importance.

• Synteny analysis: Examine gene neighborhood conservation around LJ_1698 homologs to identify functionally related genes.

• Domain architecture analysis: Compare domain organization of LJ_1698 homologs across species to identify conserved and variable regions.

• Cross-species complementation experiments: Test if LJ_1698 homologs from different species can functionally replace L. johnsonii LJ_1698 in knockout strains.

• Co-evolution analysis: Identify proteins or RNA elements that co-evolve with LJ_1698, suggesting functional interactions.

• Comparative transcriptomics: Compare the regulons affected by LJ_1698 deletion across different Lactobacillus species.

What methodological challenges exist in distinguishing direct targets of LJ_1698 from indirect effects on the L. johnsonii transcriptome?

Addressing this complex challenge requires sophisticated approaches:

• Temporally resolved experiments: Use rapid induction or inhibition systems for LJ_1698 followed by time-course sampling to distinguish primary (direct) from secondary (indirect) effects.

• Direct binding assays with purified components: Perform in vitro binding and activity assays with purified LJ_1698 and candidate RNA substrates to confirm direct targeting.

• Catalytically inactive mutants: Create point mutations that disrupt methyltransferase activity but not RNA binding, then compare binding profiles vs. methylation patterns.

• CLIP-seq with single-nucleotide resolution: Map direct binding sites at nucleotide resolution using methods like iCLIP or eCLIP.

• Nascent RNA analysis: Analyze very early transcriptional changes after LJ_1698 activation to identify primary response genes.

• In vivo RNA structure probing: Compare RNA structural changes in wild-type and knockout strains to identify RNAs whose structures are directly affected by LJ_1698 activity.

• Ribosome profiling with translational inhibitors: Use translation inhibitors to distinguish direct effects on translation from secondary transcriptional responses.

• Integration of multiple data types: Combine CLIP-seq, RNA-seq, and methylation mapping data with appropriate statistical models to distinguish direct targets from indirect effects.