Recombinant Desulfovibrio vulgaris Ribosome maturation factor RimM (rimM)

Basic Research Questions

• What is Ribosome Maturation Factor RimM in Desulfovibrio vulgaris?

  Ribosome maturation factor RimM in Desulfovibrio vulgaris is a protein involved in the maturation of the 30S ribosomal subunit. It binds to ribosomal protein S19, located in the head domain of the 30S subunit. The RimM protein is widely conserved among bacteria, and RimM-related proteins have also been found in several eukaryotic species including malaria parasites (Plasmodium falciparum and Plasmodium yoelii), the malaria mosquito (Anopheles gambiae), and the chloroplast of the plant Arabidopsis thaliana . In D. vulgaris, the full-length RimM protein consists of 176 amino acids and has two distinct domains in its N- and C-terminal regions that contribute to its function in ribosome assembly .

• How is RimM Protein Involved in Ribosome Assembly?

  RimM protein plays a specific role in ribosome assembly through several key mechanisms:

  • It associates exclusively with the free 30S subunit and not with the 30S subunit incorporated in the 70S ribosome

  • In E. coli, disruption of the rimM gene leads to accumulation of 17S rRNA (an unprocessed precursor of 16S rRNA), indicating its importance in proper rRNA processing

  • It specifically binds to ribosomal protein S19, which is categorized as a "late binder" in the assembly of the head of the 30S subunit

  • The binding of r-protein S19 with helix 33b of 16S rRNA causes conformational changes in the 3′ major domain of 16S rRNA, which RimM appears to facilitate

  • RimM is involved in the maturation of a specific region, composed of helices 31 and 33b of 16S rRNA, as well as r-proteins S13 and S19, in the head domain of the 30S subunit

  These interactions are transient and specific, making RimM an essential factor for proper ribosome maturation in bacteria, which has led to suggestions that ribosome assembly factors could serve as novel antibacterial drug targets .

• How Can I Express and Purify Recombinant RimM from D. vulgaris?

  Expressing and purifying recombinant RimM from D. vulgaris involves several critical steps:

  Expression System and Cloning:

  • Clone the full-length rimM gene (encoding all 176 amino acids) or specific domains using PCR amplification from D. vulgaris genomic DNA

  • Insert the gene into an appropriate expression vector with a suitable tag for purification

  • Transform the construct into E. coli expression host strains optimized for protein production

  • Induce protein expression using appropriate conditions (typically IPTG induction)

  Purification Protocol:

  1. Harvest cells by centrifugation and lyse using appropriate methods (sonication, French press, etc.)

  2. If the protein forms inclusion bodies (as observed with some recombinant D. vulgaris proteins), solubilize using denaturants such as guanidinium chloride (3M)

  3. For refolding, add iron (Fe(II)) anaerobically if the protein requires metal cofactors, then dilute the denaturant gradually

  4. Purify using affinity chromatography, followed by additional purification steps like ion exchange and/or size exclusion chromatography

  5. Confirm purity by SDS-PAGE (aim for >85% purity)

  6. Analyze protein activity using appropriate functional assays

  This approach has been successfully used for other recombinant proteins from D. vulgaris, such as rubrerythrin, and can be adapted for RimM purification .

• What Are the Optimal Storage Conditions for Recombinant D. vulgaris RimM?

  Optimal storage conditions for recombinant D. vulgaris RimM protein depend on the formulation and intended use:

  Formulation	Temperature	Stabilizers	Shelf Life	Notes
  Liquid form	-20°C/-80°C	5-50% glycerol (50% recommended)	6 months	Avoid repeated freeze-thaw cycles
  Lyophilized form	-20°C/-80°C	N/A	12 months	Reconstitute in deionized sterile water (0.1-1.0 mg/mL)
  Working aliquots	4°C	N/A	Up to 1 week	For immediate experimental use

  For reconstitution of lyophilized protein, it is recommended to briefly centrifuge the vial before opening to bring the contents to the bottom. Addition of glycerol to a final concentration of 50% is recommended for long-term storage of the reconstituted protein .

• What Experimental Methods Are Used to Study RimM Function?

  Several experimental approaches are employed to investigate RimM function:

  Genetic Methods:

  • Gene disruption/knockout studies to assess phenotypic effects on growth and ribosome assembly

  • Complementation assays to confirm gene function and test mutant variants

  • Site-directed mutagenesis to identify critical amino acid residues

  Biochemical Approaches:

  • Purification of native and recombinant proteins for in vitro studies

  • Pull-down assays to identify interaction partners (e.g., GST pull-down showing RimM binding to S19)

  • RNA processing analysis to examine effects on 16S rRNA maturation

  Structural Methods:

  • X-ray crystallography and NMR for high-resolution structural analysis

  • Cryo-electron microscopy to visualize RimM-ribosome complexes

  Ribosome Assembly Analysis:

  • Sucrose gradient ultracentrifugation to analyze ribosome profiles

  • Mass spectrometry to identify components of ribosome assembly intermediates

  • In vitro ribosome reconstitution assays to test the role of RimM in assembly kinetics

  These approaches can be combined to provide a comprehensive understanding of RimM function in ribosome maturation.

Advanced Research Questions

• How Can I Design High-Throughput Experiments to Study RimM Across Bacterial Species?

  Designing high-throughput experiments to study RimM across bacterial species requires standardized approaches that can be efficiently scaled and applied to diverse organisms:

  Vector Design and Construction Strategy:

  • Develop a Gateway-based cloning system for modular construction of expression and knockout vectors

  • Use SLIC (sequence and ligation-independent cloning) to assemble custom constructs with standardized components

  • Design vectors with reusable and interchangeable DNA "parts" that can be applied across species

  • Incorporate appropriate selection markers for different bacterial hosts

  Transformation and Selection Protocol:

  • Optimize electroporation conditions for each target species

  • For difficult-to-transform anaerobes like D. vulgaris, use specialized recovery media:

    • MOYLS4 medium with 15 g/liter agar containing G418 (400 μg/ml)

    • Add reductants (sodium thioglycolate and titanium citrate) for anaerobic growth conditions

  • Extend incubation times for slow-growing species (5+ days for D. vulgaris)

  Experimental Design Table for Multi-Species Analysis:

  Species	Vector Construction	Transformation Method	Selection Marker	Recovery Conditions	Verification Method
  D. vulgaris	SLIC with pUC19-based vectors	Electroporation	G418 (kanamycin analog)	Anaerobic, MOYLS4, 30°C, 5 days	Southern blot, PCR
  E. coli	λ red recombination	Chemical transformation	Kanamycin (50 μg/ml)	Aerobic, LB, 37°C, 1 day	Colony PCR, sequencing
  Other species	TOPO/Gateway cloning	Species-specific methods	Appropriate antibiotics	Optimized for each species	Western blot, sequencing

  This standardized approach enables efficient manipulation of the rimM gene across diverse bacterial species, facilitating comparative functional studies .

• What Are the Best Approaches for Analyzing RimM Interactions with Ribosomal Components?

  Analyzing RimM interactions with ribosomal components requires a multi-faceted approach combining several techniques:

  Affinity-Based Methods:

  • Sequential Peptide Affinity (SPA) tagging: Create chromosomal SPA-tagged RimM constructs for gentle purification of intact complexes

  • Tandem Affinity Purification (TAP): Isolate RimM complexes under native conditions

  • GST pull-down assays: Previously used successfully to demonstrate RimM binding to r-protein S19

  Crosslinking and Mass Spectrometry:

  • Use chemical crosslinkers to stabilize transient interactions

  • Identify crosslinked peptides by mass spectrometry to map interaction interfaces

  • Analyze protein composition of isolated complexes using LC-MS/MS

  Structural Biology Approaches:

  • Cryo-EM analysis of RimM-30S complexes at different assembly stages

  • NMR spectroscopy for detecting dynamic interactions in solution

  In Vivo Localization:

  • Fluorescent protein fusion constructs to visualize RimM localization

  • Co-localization studies with ribosomal markers

  • Super-resolution microscopy for detailed spatial distribution

  Functional Validation Experiments:

  • Mutagenesis of identified interaction sites followed by functional testing

  • Competition assays with peptides mimicking interaction interfaces

  • Complementation experiments with domain-swapped chimeric proteins

  These approaches can be integrated to build a comprehensive map of RimM interactions with S19, 16S rRNA, and other potential binding partners .

• How Does RimM Function Compare Between D. vulgaris and Other Bacterial Species?

  Comparing RimM function across bacterial species reveals both conserved mechanisms and species-specific adaptations:

  Evolutionary Conservation:

  • RimM is widely conserved among bacteria, indicating its fundamental importance in ribosome assembly

  • RimM-related proteins have also been found in eukaryotic species, including malaria parasites and plant chloroplasts, suggesting ancient evolutionary origins

  Functional Similarities:

  • In both E. coli and D. vulgaris, RimM associates specifically with the free 30S subunit

  • The core function in facilitating 30S subunit head domain assembly appears conserved

  • Binding to ribosomal protein S19 is observed across species

  Species-Specific Adaptations:

  • E. coli rimM disruption leads to accumulation of 17S rRNA and affects a specific region composed of helices 31 and 33b of 16S rRNA

  • D. vulgaris RimM may have adaptations related to its anaerobic lifestyle and unique ribosome composition

  • Sequence variations in the C-terminal domain may reflect species-specific rRNA interactions

  Structural Comparison:

  • While the two-domain architecture is conserved, specific residues at interaction interfaces may vary

  • Domain sizes and interdomain flexibility can differ between species

  Understanding these similarities and differences is crucial for developing species-specific antibiotic targeting strategies, as ribosome assembly factors have been proposed as novel antibacterial drug targets .

• What Experimental Design Is Most Appropriate for Studying RimM's Role in Ribosome Assembly?

  Designing experiments to study RimM's role in ribosome assembly requires careful consideration of several factors:

  Key Experimental Design Elements:

  1. Control Selection:

     • Include appropriate wild-type controls for comparison

     • Use inactive RimM mutants as negative controls

     • Consider complementation controls (ΔrimM + plasmid-expressed RimM)

  2. Variable Definition and Measurement:

     • Independent variables: RimM concentration, mutations, domain deletions

     • Dependent variables: 30S assembly completion, rRNA processing state, growth rate

     • Control variables: temperature, ionic conditions, other assembly factors

  3. Randomization and Replication:

     • Minimum three biological replicates for statistical validity

     • Multiple technical replicates to account for measurement variability

     • Randomize experimental order to minimize systematic errors

  Sample Experimental Design Table:

  Experimental Approach	Design Structure	Controls	Sample Size	Data Analysis	Expected Outcomes
  In vitro reconstitution	Factorial (RimM variants × time points)	No RimM, heat-inactivated RimM	3 biological × 3 technical replicates	ANOVA with Tukey post-hoc	Identify assembly intermediates that accumulate without RimM
  In vivo depletion	Time course with repeated measures	Wild-type strain, non-target depletion	4 biological replicates	Mixed-effects model	Characterize physiological effects of RimM depletion
  Structure-function analysis	Systematic mutagenesis	Wild-type RimM, unrelated protein mutants	3 replicates per mutant	Multiple regression	Map functional regions of RimM

  This structured approach ensures experimental rigor while addressing the complex, dynamic nature of ribosome assembly .

• How Can I Resolve Contradictory Data on RimM Function from Different Studies?

  Resolving contradictory data on RimM function requires systematic evaluation of methodological differences and careful integration of findings:

  Sources of Experimental Variation:

  1. Species differences: RimM may have species-specific functions or interactions

  2. Experimental conditions: In vitro vs. in vivo approaches yield different insights

  3. Methodological approaches: Different techniques have varying sensitivities and limitations

  4. Protein constructs: Full-length vs. truncated proteins or different tagging strategies

  Systematic Analysis Framework:

  Study Component	Documentation Requirements	Comparison Metrics	Integration Strategy
  Experimental system	Species, strain, growth conditions	Degree of similarity to natural conditions	Weight findings by physiological relevance
  RimM constructs	Sequence, domains, tags, expression level	Structural integrity, activity validation	Compare equivalent protein forms
  Interaction detection	Method sensitivity, controls, replication	False positive/negative rates	Prioritize results confirmed by multiple methods
  Functional assays	Endpoint measurements, time resolution	Direct vs. indirect measurements	Focus on direct functional readouts

  Resolution Strategies:

  1. Direct experimental comparison: Test contradictory findings using identical conditions

  2. Meta-analysis: Statistically combine results across studies while accounting for heterogeneity

  3. Bayesian integration: Weight evidence based on methodological rigor and direct relevance

  4. Third-party validation: Have independent labs replicate key contradictory findings

  This structured approach helps distinguish genuine biological differences from methodological artifacts .

• What Approaches Can Be Used to Study RimM in Challenging Bacterial Species Like D. vulgaris?

  Studying RimM in challenging bacterial species like D. vulgaris requires specialized techniques adapted for anaerobic, slow-growing organisms:

  Genetic Manipulation Strategies:

  1. Custom Vector Design:

     • Develop suicide vectors carrying the desired modifications (tags, mutations, deletions)

     • Include selectable markers functional in D. vulgaris (G418 for kanamycin resistance)

     • Use homologous recombination for chromosomal integration

  2. Transformation Protocol for Anaerobes:

     • Perform electroporation under anaerobic conditions

     • Use specialized recovery media with appropriate reductants:

       • Sodium thioglycolate (1.2 mM) added before autoclaving

       • Titanium citrate (1.2 mM) prepared under nitrogen and added just before use

     • Extended incubation times (typically 5 days) for colony formation

  3. Verification Methods:

     • Southern blotting to confirm chromosomal modifications

     • PCR verification of genomic integrations

     • Western blotting with anti-RimM antibodies

     • Immunoprecipitation to verify protein expression and interactions

  Protein Analysis Under Anaerobic Conditions:

  1. Anaerobic Protein Expression:

     • Label proteins with radioisotopes (35S) in minimal media under nitrogen atmosphere

     • Monitor expression in different oxygen conditions to assess regulation

  2. Protein-Protein Interaction Analysis:

     • Prepare cell extracts under anaerobic conditions

     • Use pretreated antibodies to eliminate cross-reactions with other proteins

     • Analyze complexes by SDS-PAGE and autoradiography or fluorography

  These specialized approaches overcome the challenges associated with studying RimM in anaerobic bacteria like D. vulgaris .

• How Can Statistical Approaches Enhance RimM Functional Studies?

  Appropriate statistical approaches significantly enhance the rigor and interpretability of RimM functional studies:

  Experimental Design Statistics:

  1. Power Analysis and Sample Size Determination:

     • Calculate required sample sizes based on expected effect sizes

     • Consider both biological and technical variability sources

     • Determine appropriate replication levels (n = f(α, β, effect size))

  2. Randomization and Blocking:

     • Implement randomized complete block designs to control for nuisance variables

     • Use factorial designs to efficiently test multiple variables simultaneously

     • Apply latin square or split-plot designs for complex experimental setups

  Data Analysis Approaches:

  1. For Comparative Studies:

     • ANOVA with appropriate post-hoc tests for comparing multiple conditions

     • Linear mixed-effects models to account for nested experimental structures

     • Non-parametric alternatives when assumptions are violated

  2. For Time-Course Experiments:

     • Repeated measures ANOVA or mixed models for longitudinal data

     • Growth curve fitting using non-linear regression models

     • Time-to-event analysis for developmental milestones

  3. For High-Dimensional Data:

     • Multiple testing correction (FDR, Bonferroni) for proteomics/transcriptomics

     • Dimension reduction techniques (PCA, t-SNE) for visualizing complex datasets

     • Cluster analysis to identify patterns in multi-parameter experiments

  Reproducibility Enhancement:

  1. Preregistration of Analysis Plans:

     • Define hypotheses and analysis strategies before data collection

     • Distinguish confirmatory from exploratory analyses

     • Minimize p-hacking and HARKing (Hypothesizing After Results are Known)

  2. Effect Size Reporting:

     • Report standardized effect sizes with confidence intervals

     • Focus on biological significance rather than just statistical significance

     • Use meta-analytic thinking to integrate new results with existing knowledge

  These statistical approaches increase the rigor and reproducibility of RimM functional studies .

• How Do I Design a Data Table for RimM Expression and Purification Experiments?

  Designing effective data tables for RimM expression and purification experiments requires careful organization of experimental variables and measurements:

  Key Elements of Experimental Data Tables:

  1. Clear Title: Explicitly state the purpose (e.g., "Effect of Induction Conditions on D. vulgaris RimM Expression Yield")

  2. Independent Variables: Place in leftmost column (e.g., temperature, induction time, strain)

  3. Dependent Variables: Organize in columns for each measurement (e.g., protein yield, purity, activity)

  4. Multiple Trials: Include separate columns for each replicate

  5. Derived Values: Add columns for calculated metrics (e.g., averages, standard deviations)19

  Sample Data Table for RimM Expression Optimization:

  Temperature (°C)	IPTG Concentration (mM)	Trial 1 Yield (mg/L)	Trial 2 Yield (mg/L)	Trial 3 Yield (mg/L)	Average Yield (mg/L)	Purity by SDS-PAGE (%)
  18	0.1	14.3	15.2	14.7	14.7	82
  18	0.5	18.6	19.2	17.8	18.5	85
  25	0.1	22.3	21.8	23.1	22.4	78
  25	0.5	25.6	26.1	24.9	25.5	75
  37	0.1	12.5	11.8	13.0	12.4	65
  37	0.5	10.2	11.1	9.8	10.4	58

  Sample Data Table for RimM Purification Steps:

  Purification Step	Volume (mL)	Total Protein (mg)	RimM Content (%)	RimM Yield (mg)	Cumulative Recovery (%)	Activity (units/mg)
  Crude Extract	120	450	15	67.5	100	35
  Affinity Chromatography	25	85	65	55.3	82	120
  Ion Exchange	15	42	90	37.8	56	245
  Size Exclusion	10	32	>95	30.4	45	280

  Proper data table design enhances clarity, facilitates analysis, and ensures reproducibility in RimM research19 .