Recombinant Bifidobacterium adolescentis tRNA dimethylallyltransferase (miaA)

How does the MiaA enzyme recognize its target tRNAs in bacterial systems?

MiaA specifically recognizes tRNAs containing the A36A37A38 motif in their anticodon stem-loop region . In bacterial systems, this typically includes tRNAs for Leu, Phe, Ser, Cys, Trp, and Tyr—tRNAs that decode codons with U in the first position . The enzyme binds to these specific tRNAs and catalyzes the transfer of the isopentenyl group from DMAPP exclusively to the A37 position, creating the i6A modification . This recognition mechanism is highly specific and conserved across bacterial species, ensuring that only appropriate tRNAs undergo this critical modification.

What experimental techniques are commonly used to confirm MiaA activity in recombinant systems?

The most common approach to confirm MiaA activity involves:

• In vitro transcription of target tRNAs: PCR amplification of tRNA genes with T7 promoter sequences, followed by transcription using T7 RNA polymerase and appropriate nucleotide mixtures .

• Radioactive labeling: Incorporation of α-32P GTP during transcription to visualize tRNAs by autoradiography .

• Isopentenyltransferase assay: Incubation of labeled tRNAs with purified recombinant MiaA in the presence or absence of DMAPP (substrate) .

• RNase digestion and mobility shift analysis: Digestion of modified tRNAs with RNase T1 (which cleaves at the 3' end of all guanosines) followed by separation on polyacrylamide gels to detect mobility shifts in fragments containing the i6A modification .

• Western blotting: Confirmation of recombinant MiaA expression using appropriate antibodies (e.g., anti-FLAG for tagged versions) .

What are the optimal conditions for expressing and purifying recombinant B. adolescentis MiaA, and how do they differ from those used for other bacterial MiaA enzymes?

For optimal expression and purification of recombinant B. adolescentis MiaA:

Expression system optimization:

• Vector selection: pNit3xFLAG or similar inducible expression vectors with appropriate affinity tags (FLAG, His, etc.) have proven effective for bacterial MiaA expression .

• Host selection: While E. coli is commonly used, expression in Mycobacterium smegmatis has proven successful for mycobacterial MiaA and may offer advantages for B. adolescentis MiaA due to similar G+C content and protein folding environments .

• Induction conditions: For Mycobacterium MiaA, 5μM isovaleronitrile (IVN) for 16 hours at 37°C yielded optimal expression . B. adolescentis MiaA might require modified conditions, potentially with lower temperatures (28-30°C) to improve protein solubility.

Purification protocol:

• Cell lysis: Mechanical disruption using zirconium beads (0.1mm) in multiple short cycles with intervals on ice .

• Affinity purification: Immunoprecipitation using affinity gel beads (e.g., FLAG-M2) with overnight incubation at 4°C .

• Elution conditions: 0.1M glycine (pH 3.5) with immediate neutralization using 1/10th volume 1M Tris-HCl (pH 7.4) .

Key differences from other bacterial MiaA enzymes:

• B. adolescentis, as a gut commensal with different environmental adaptations than pathogens like M. tuberculosis, may have different protein stability profiles requiring modified buffer compositions (potentially with added glycerol or reducing agents).

• The optimal pH and salt concentration may differ based on the natural cytoplasmic environment of Bifidobacterium species.

How can researchers design comprehensive tRNA target profiling for B. adolescentis MiaA using bioinformatic and experimental approaches?

A comprehensive approach to profile tRNA targets for B. adolescentis MiaA should combine both bioinformatic prediction and experimental validation:

Bioinformatic prediction:

• Genome mining: Extract all tRNA sequences from the B. adolescentis genome using tRNAscan-SE or similar tools.

• Motif identification: Screen for tRNAs containing the A36A37A38 motif in the anticodon loop.

• Comparative genomics: Compare with known MiaA targets in related bacteria (similar to the approach used for M. tuberculosis in Table 1) .

Experimental validation:

• In vitro transcription: Generate candidate tRNAs with T7 promoter-based systems .

• Isopentenyltransferase assay: Incubate purified recombinant MiaA with radiolabeled tRNAs in the presence of DMAPP .

• RNase T1 digestion and gel electrophoresis: Identify mobility shifts in fragments containing A37 .

• Mass spectrometry: For definitive identification of the i6A modification at specific positions.

• RNA-Seq with specialized protocols: For direct detection of modified nucleosides in cellular tRNA pools.

What approaches can resolve common challenges in detecting the i6A modification in B. adolescentis tRNAs with high G+C content?

The high G+C content in B. adolescentis genome presents specific challenges for i6A modification detection, similar to issues encountered with M. tuberculosis . To overcome these challenges:

Improved RNase digestion strategies:

• Alternative RNases: Instead of relying solely on RNase T1 (which generates very small fragments in G+C rich organisms), use RNase A (cuts after C and U) or RNase V1 (cuts double-stranded regions) to generate larger, more distinguishable fragments.

• Combined enzyme approach: Sequential or parallel digestions with different RNases to create a more informative fragmentation pattern.

Advanced detection methods:

• 2D gel electrophoresis: Separating RNA fragments first by size and then by composition to better resolve mobility shifts.

• High-resolution mass spectrometry: LC-MS/MS approaches with RNA-specific optimization can directly detect i6A modifications without relying on fragment mobility shifts.

• Antibody-based detection: Development of antibodies specific to i6A for immunoprecipitation of modified fragments.

Synthetic reference standards:

• Generate synthetic RNA oligonucleotides with and without i6A modifications to serve as positive and negative controls for each target tRNA fragment.

Computational prediction refinement:

• Develop algorithms that account for G+C bias in predicting RNase digestion patterns and fragment characteristics.

How does the function of MiaA in B. adolescentis compare to its role in pathogens like M. tuberculosis or E. coli regarding stress response and adaptation?

The functional role of MiaA varies significantly between commensal bacteria like B. adolescentis and pathogens:

In pathogens:

• In E. coli, MiaA is essential for stress transcription factor RpoS translation and successful transition to stationary phase .

• In Extraintestinal Pathogenic E. coli (ExPEC), MiaA is essential for gut colonization and provides tolerance to oxidative, nitrosative, and osmotic stresses .

• In Shigella flexneri, MiaA is critical for virulence gene expression, with miaA mutations resulting in avirulent phenotypes .

• In M. tuberculosis and M. bovis BCG, tRNA modifications including i6A likely help the bacteria survive host-induced stress conditions like hypoxia, nutrient deprivation, and oxidative stress .

In B. adolescentis (predicted differences):

• As a commensal gut bacterium, B. adolescentis MiaA likely plays a crucial role in:

  • Adaptation to the fluctuating nutrient environment of the human intestine

  • Mediating interactions with the host immune system without triggering inflammatory responses

  • Facilitating co-metabolism with other gut commensals, particularly in the degradation of plant-derived glycans like xylan

  • Potentially influencing the expression of genes involved in health-promoting activities

Key research directions:

• Creating miaA knockout strains in B. adolescentis to assess growth defects under various stress conditions

• Comparative transcriptomics and proteomics between wild-type and miaA mutants to identify differentially expressed genes

• Co-culture experiments with human intestinal cells to evaluate how MiaA influences host-microbe interactions

What methodological approaches can determine if MiaA activity influences B. adolescentis' ability to survive in the human gastrointestinal tract?

To investigate MiaA's influence on B. adolescentis gastrointestinal survival:

In vitro gastrointestinal simulation models:

• Static digestion models: Expose wild-type and miaA mutant strains to sequential treatments mimicking gastric (low pH, pepsin) and intestinal (bile salts, pancreatin) conditions.

• Dynamic digestion simulators: Use systems like the TNO Intestinal Model (TIM) or Simulator of Human Intestinal Microbial Ecosystem (SHIME) for more realistic testing.

• Stress resistance assays: Compare survival under specific stresses (acid, bile, oxidative, osmotic) that bacteria encounter in the GI tract.

Cell culture interaction studies:

• Adhesion assays: Quantify adhesion of wild-type and miaA mutants to intestinal cell lines (Caco-2, HT-29).

• Transwell systems: Assess bacterial translocation and epithelial barrier function effects.

• Mucus penetration models: Evaluate ability to penetrate mucus layers using ex vivo mucus or mucus-producing cell models.

Animal model experiments:

• Gnotobiotic mouse colonization: Monitor colonization efficiency of wild-type versus miaA mutant strains.

• Competitive index studies: Co-administer wild-type and mutant strains to assess relative fitness in vivo.

• Metatranscriptomics: Analyze gene expression patterns of the strains during intestinal colonization.

Microbiome interaction studies:

• Co-cultivation experiments: Similar to those performed with B. adolescentis PRL2023 , examine how MiaA affects interactions with other gut commensals.

• Metabolite profiling: Analyze differences in metabolite production during growth on different carbon sources.

How can researchers investigate the translational effects of i6A modification in B. adolescentis using ribosome profiling or similar techniques?

To investigate translational effects of i6A modification in B. adolescentis:

Ribosome profiling experimental design:

• Strain preparation: Generate isogenic strains (wild-type and miaA knockout/mutant).

• Growth conditions: Culture under relevant conditions, including normal growth and stress conditions (nutrient limitation, bile exposure, etc.).

• Ribosome footprinting: Harvest cells, treat with translation inhibitors, digest with nucleases to isolate ribosome-protected fragments.

• Library preparation: Process ribosome-protected fragments for next-generation sequencing.

• Parallel RNA-Seq: Perform total RNA sequencing to normalize for transcript abundance.

Data analysis approaches:

• Codon-specific translation efficiency: Calculate translation efficiency scores for each codon, with special attention to codons read by i6A-modified tRNAs (UNN codons).

• A-site/P-site/E-site occupancy: Analyze ribosome occupancy at specific codon positions to identify potential pausing sites.

• Differential gene expression: Identify genes whose translation is most affected by loss of i6A modification.

• Metagene analysis: Generate profiles of ribosome distribution across mRNAs to identify global translation patterns.

Advanced methods to complement ribosome profiling:

• Proteomics: Compare protein expression profiles between wild-type and miaA mutant strains.

• tRNA microarrays or tRNA-Seq: Quantify charged and uncharged tRNA populations.

• In vitro translation systems: Reconstitute translation using ribosomes and modified/unmodified tRNAs to measure decoding efficiency.

• PUNCH-P (puromycin-associated nascent chain proteomics): Identify newly synthesized proteins to directly assess translation outputs.

What bioinformatic approaches can identify differences in MiaA protein structure and function across Bifidobacterium species compared to other gut bacteria?

To compare MiaA across Bifidobacterium species and other gut bacteria:

Sequence-based analyses:

• Multiple sequence alignment: Align MiaA protein sequences from diverse Bifidobacterium species and other gut bacteria using tools like MUSCLE, MAFFT, or Clustal Omega.

• Phylogenetic tree construction: Generate maximum likelihood or Bayesian trees to visualize evolutionary relationships among MiaA proteins.

• Conservation analysis: Identify highly conserved residues, particularly in the active site and tRNA binding regions.

• Selection pressure analysis: Calculate dN/dS ratios to identify regions under positive, neutral, or purifying selection.

Structural bioinformatics:

• Homology modeling: Generate structural models of B. adolescentis MiaA based on available crystal structures (e.g., E. coli MiaA).

• Protein-protein and protein-tRNA docking: Predict interaction interfaces with target tRNAs.

• Molecular dynamics simulations: Compare structural stability and flexibility of MiaA proteins from different species.

• Active site comparison: Analyze differences in substrate binding pockets that might affect enzyme kinetics.

Functional domain analysis:

• Domain architecture: Compare the organization of functional domains across species.

• Conserved motif identification: Identify species-specific or clade-specific sequence motifs.

• Co-evolution analysis: Identify correlated mutations that might indicate functional relationships.

Ecological context integration:

• Habitat association analysis: Correlate MiaA sequence features with ecological niches (infant gut, adult gut, probiotic strains).

• Genomic context analysis: Examine gene neighborhoods and potential operonic structures.

• Horizontal gene transfer detection: Identify potential instances of HGT that might have shaped MiaA evolution.

How should researchers design experiments to compare the substrate specificity and catalytic efficiency of MiaA enzymes from different Bifidobacterium strains?

To compare MiaA enzymes from different Bifidobacterium strains:

Enzyme preparation:

• Construct design: Create expression vectors for MiaA from multiple Bifidobacterium strains with identical tags and expression systems.

• Parallel purification: Purify all enzymes using identical protocols to minimize method-based variations.

• Quality control: Verify protein purity, folding status, and stability by SDS-PAGE, circular dichroism, and thermal shift assays.

Substrate preparation:

• tRNA diversity: Generate a panel of potential target tRNAs from different Bifidobacterium species.

• DMAPP alternatives: Test different alkyl donor substrates to assess substrate promiscuity.

Kinetic analysis methodology:

• Initial rate determination: Measure reaction rates at varying substrate concentrations.

• Michaelis-Menten parameters: Calculate Km, kcat, and kcat/Km for each enzyme-substrate pair.

• Competition assays: When multiple tRNAs are present simultaneously, determine substrate preferences.

• pH and temperature profiles: Compare optimal conditions and stability ranges.

Experimental design for comparative analysis:

Advanced analytical methods:

• Pre-steady state kinetics: Measure rapid kinetics using stopped-flow or quench-flow techniques.

• NMR spectroscopy: Monitor reaction progress in real-time.

• Mass spectrometry: Precisely quantify modification levels across different tRNA substrates.

What approaches can elucidate if horizontal gene transfer has influenced the evolution of miaA genes in Bifidobacterium adolescentis compared to other gut microbes?

To investigate potential horizontal gene transfer (HGT) of miaA genes:

Sequence-based detection methods:

• Phylogenetic incongruence: Compare miaA gene trees with species trees or core gene trees; significant discrepancies may indicate HGT.

• Compositional bias analysis: Examine GC content, codon usage, and oligonucleotide frequencies of miaA genes compared to the genomic average.

• Alien gene detection algorithms: Apply tools like Alien_Hunter, IslandViewer, or SIGI-HMM to identify genomic islands containing miaA.

• Distribution analysis: Map presence/absence patterns of miaA across related species; patchy distribution may suggest HGT.

Comparative genomic approaches:

• Synteny analysis: Compare gene neighborhoods around miaA across different species; disrupted synteny may indicate insertion via HGT.

• Mobile genetic element association: Look for proximity to transposons, insertion sequences, or phage-related genes.

• Flanking sequence analysis: Identify potential integration sites or remnants of recombination events.

Evolutionary rate analyses:

• dN/dS ratio calculation: Regions acquired through HGT often show distinct evolutionary rates.

• Relative rate tests: Compare substitution rates between miaA and housekeeping genes.

• Molecular clock analyses: Date divergence events to identify anomalously recent acquisitions.

Case study design for B. adolescentis:

• Dataset construction: Compile miaA sequences from diverse Bifidobacterium species and other gut bacteria representing different phyla.

• Multi-method approach: Apply several HGT detection methods and look for consensus.

• Experimental validation: If HGT is suspected, examine functional differences in the transferred MiaA compared to ancestral versions.

How can researchers use MiaA as a molecular marker to track B. adolescentis strains in complex microbiome samples, and what methodological challenges must be addressed?

Using MiaA as a molecular marker for B. adolescentis strain tracking:

Method development considerations:

• Primer/probe design:

  • Target miaA gene regions with sufficient strain-level polymorphism while maintaining species specificity

  • Account for potential cross-reactivity with related Bifidobacterium species

  • Design nested PCR approaches for increased sensitivity in complex samples

• Sequencing-based approaches:

  • Develop amplicon sequencing strategies targeting variable regions of miaA

  • Design metagenomic sequencing and bioinformatic pipelines to extract strain-specific miaA variants

  • Create strain-specific SNP panels for digital PCR or targeted resequencing

• Protein-based detection:

  • Develop antibodies against strain-specific MiaA epitopes

  • Establish mass spectrometry methods to detect strain-specific peptide markers

Methodological challenges and solutions:

Validation strategies:

• In vitro mock communities: Create defined mixtures of known B. adolescentis strains and background microbiota.

• Spiking experiments: Add known quantities of target strains to real microbiome samples.

• Multi-method cross-validation: Compare results from different detection methods.

• Longitudinal tracking: Monitor strain persistence in human subjects over time.

What experimental designs can determine if the i6A modification pattern in B. adolescentis changes in response to different gut environmental conditions?

To investigate environmental responsiveness of i6A modification:

Experimental conditions to test:

• Nutrient availability: Growth in media with different carbon sources (glucose, fructose, complex polysaccharides).

• Gastrointestinal stressors: Exposure to bile acids, varying pH, oxygen gradients.

• Co-culture conditions: Growth with different gut commensals or pathogens.

• Host-derived factors: Exposure to mucins, antimicrobial peptides, immune factors.

• Prebiotics: Growth on different prebiotic substrates (FOS, GOS, XOS).

Analytical approaches:

• tRNA isolation protocol optimization:

  • Rapid extraction methods to minimize degradation

  • Acidic phenol extraction to preserve modifications

  • Size selection to enrich for tRNA fraction

• Modification detection methods:

  • Liquid chromatography-mass spectrometry (LC-MS/MS)

  • High-resolution RNA sequencing with modification-sensitive chemistry

  • Antibody-based detection of i6A modifications

  • RNase digestion and fragment analysis

• Quantitative assessment:

  • Absolute quantification using synthetic standards

  • Relative quantification across different conditions

  • Site-specific modification rates for individual tRNAs

Parallel analyses to correlate with modification changes:

• Transcriptomics: RNA-Seq to identify gene expression changes

• Proteomics: Quantitative proteomics to detect translation effects

• miaA expression analysis: qRT-PCR to monitor miaA transcript levels

• MiaA enzyme activity assays: Direct measurement of enzyme activity

• Growth and survival phenotyping: Correlate modification changes with fitness metrics

How might genetic manipulation of the miaA gene in B. adolescentis impact its potential as a probiotic strain, and what methodological approaches can evaluate these effects?

Investigating impacts of miaA manipulation on probiotic potential:

Genetic manipulation strategies:

• Gene knockout: CRISPR-Cas9 or homologous recombination to delete miaA

• Controlled expression: Inducible promoter systems to modulate miaA expression levels

• Point mutations: Site-directed mutagenesis to alter enzyme activity or specificity

• Heterologous expression: Introduction of miaA variants from other species

Probiotic property assessment pipeline:

• Safety evaluation:

  • Antibiotic resistance profiling

  • Production of toxic metabolites

  • Genetic stability assessment

  • Translocation potential in gut models

• Survival and persistence:

  • Acid and bile tolerance assays

  • Adherence to intestinal cell lines

  • Competitive fitness in mixed cultures

  • In vivo colonization studies in animal models

• Health-promoting functions:

  • Production of beneficial metabolites (SCFAs, vitamins)

  • Immunomodulatory effects on human cell lines

  • Antagonism against pathogens

  • Anti-inflammatory potential

• Ecological interactions:

  • Co-culture experiments with key gut commensals

  • Co-metabolism of dietary components

  • Competitive exclusion of pathogens

  • Cross-feeding relationships

Specialized methodology for B. adolescentis:

• Plant glycan utilization: Assess ability to metabolize xylan and other plant-derived glycans through enzymatic assays and growth profiling .

• Microbe-microbe interactions: Evaluate co-metabolism patterns with other gut commensals using metabolomics approaches .

• Host cell interaction models: Develop specialized assays to measure adherence, immune signaling, and barrier function effects.

• Prototype strain comparison: Benchmark engineered strains against prototype strains like B. adolescentis PRL2023 .

Experimental design for comparative assessment:

What are the most significant technical challenges in working with recombinant B. adolescentis MiaA, and what innovative solutions have researchers developed?

Major technical challenges and innovative solutions:

Challenge 1: Protein solubility and activity

• Issues: Bifidobacterium proteins often form inclusion bodies in E. coli expression systems due to differences in codon usage and folding environments.

• Innovative solutions:

  • Expression in related host systems (other Bifidobacterium species or Lactococcus lactis)

  • Use of specialized E. coli strains with rare codon supplementation

  • Fusion with solubility-enhancing tags (MBP, SUMO, thioredoxin)

  • Low-temperature induction protocols (16-20°C) with extended expression times

  • Co-expression with chaperone systems specific to Gram-positive bacteria

Challenge 2: Enzyme assay sensitivity

• Issues: Traditional gel shift assays for i6A detection often have limited sensitivity, especially with high G+C content tRNAs .

• Innovative solutions:

  • Development of fluorescence-based activity assays using labeled DMAPP analogs

  • LC-MS/MS methods for direct detection of modified nucleosides

  • Real-time NMR spectroscopy to monitor reaction progression

  • Microfluidic approaches for single-molecule enzyme kinetics

  • CRISPR-Cas13-based detection systems for specific modified RNA sequences

Challenge 3: Anaerobic working conditions

• Issues: B. adolescentis is anaerobic, and some proteins may be oxygen-sensitive.

• Innovative solutions:

  • Anaerobic expression systems and purification workflows

  • Addition of reducing agents throughout purification process

  • Oxygen-scavenging enzyme systems in reaction buffers

  • Microfluidic devices with controlled atmospheric conditions

  • Rapid work protocols to minimize oxygen exposure

Challenge 4: Substrate availability

• Issues: Limited commercial availability of specific tRNA substrates and DMAPP.

• Innovative solutions:

  • Development of simplified substrate mimics for high-throughput screening

  • Enzymatic synthesis of DMAPP using mevalonate pathway enzymes

  • Cell-free transcription systems for tRNA production

  • Chemoenzymatic approaches for generating modified tRNA standards

  • Computational design of minimal tRNA substrates retaining MiaA recognition elements

How can researchers overcome the unique challenges of studying tRNA modifications in high G+C content bacteria like B. adolescentis?

Strategies for studying tRNA modifications in high G+C content bacteria:

RNA isolation optimization:

• Modified extraction protocols:

  • Hot phenol extraction with specialized buffers

  • Use of chaotropic agents to minimize secondary structure formation

  • Inclusion of denaturants during initial lysis steps

  • Small RNA-specific isolation kits with modifications for high G+C content

• Enrichment strategies:

  • Size-based purification optimized for tRNA range

  • Affinity-based approaches using complementary oligonucleotides

  • Chemical enrichment of modified nucleosides

Analysis method adaptations:

• RNase digestion strategies:

  • Use of RNases with different specificities to generate optimal fragment sizes

  • Controlled partial digestion to improve fragment diversity

  • Development of G+C-optimized digestion protocols

• Sequencing enhancements:

  • Modified reverse transcription conditions to resolve G+C-rich regions

  • Use of thermostable reverse transcriptases

  • Addition of denaturants or secondary structure disruptors

  • Specialized adapter designs for tRNA sequencing

• Chromatographic separation:

  • Optimization of HPLC and LC conditions for high G+C fragments

  • Temperature-controlled chromatography to minimize secondary structure issues

  • Pulsed-field gel electrophoresis for improved resolution

Innovative detection approaches:

• Nanopore direct RNA sequencing:

  • Direct detection of modified bases without reverse transcription

  • Machine learning algorithms trained on high G+C content RNA signals

• Mass spectrometry adaptations:

  • Ion fragmentation methods optimized for G+C-rich oligonucleotides

  • Development of specialized internal standards for quantification

  • Targeted multiple reaction monitoring for specific modified nucleosides

• Microscopy-based methods:

  • Single-molecule fluorescence approaches

  • Atomic force microscopy to detect structural changes in modified vs. unmodified tRNAs

What next-generation methodologies might revolutionize the study of MiaA-mediated tRNA modifications in the context of the human gut microbiome?

Emerging methodologies with transformative potential:

Single-cell technologies:

• Single-cell RNA modification profiling:

  • Microfluidic-based isolation of individual bacterial cells from gut samples

  • Single-cell RNA sequencing with modification detection capabilities

  • Correlation of modification profiles with bacterial identity and metabolic state

• In situ microscopy:

  • Fluorescent probes specific for i6A modifications

  • FISH-based detection of modified tRNAs within intact bacteria

  • Super-resolution microscopy to visualize modification dynamics

Direct sampling approaches:

• Ex vivo gut models:

  • Human intestinal organoids colonized with defined microbial communities

  • Sampling and analysis of tRNA modifications under controlled conditions

  • Real-time monitoring of modification dynamics during environmental shifts

• In vivo sampling:

  • Development of gut-targeted sampling devices for minimally invasive collection

  • Rapid preservation methods to capture modification status accurately

  • Direct analysis from fecal samples with minimal processing

Computational advances:

• Machine learning for modification prediction:

  • Algorithms trained on known modification patterns to predict sites in novel tRNAs

  • Integration of secondary structure information with sequence data

  • Models accounting for species-specific and condition-specific modification preferences

• Multi-omics integration:

  • Frameworks linking tRNA modifications to transcriptomics, proteomics, and metabolomics

  • Network analysis tools to identify functional consequences of modification changes

  • Predictive models of how modifications influence translation efficiency

Revolutionary analytical methods:

• Nanopore arrays:

  • Massively parallel direct RNA sequencing

  • Real-time detection of modifications without chemical conversion

  • Continuous monitoring of modification status in living bacterial systems

• CRISPR-based detection:

  • Cas13-based systems engineered to specifically recognize modified bases

  • In vivo reporters of modification status

  • High-throughput screening platforms for modification-affecting compounds

• Synthetic biology approaches:

  • Engineered bacterial reporters that signal modification status

  • Artificial tRNA systems with fluorescent or enzymatic outputs

  • Modular systems for systematic investigation of modification effects