Recombinant Bacillus licheniformis 30S ribosomal protein S19 (rpsS)

What is the function of 30S ribosomal protein S19 (rpsS) in Bacillus licheniformis and how does it compare to other Bacillus species?

The 30S ribosomal protein S19 (rpsS) in Bacillus licheniformis is a crucial component of the small ribosomal subunit involved in protein translation. According to phylogenetic studies, S19 belongs to a group of ribosomal proteins with partial phylogenetic significance, capable of reproducing the major branches of the 16S rRNA phylogenetic tree but lacking precision in the correct placement of one or two Bacillus species . This characteristic makes it valuable for evolutionary studies while indicating its functional conservation across the genus.

Research methodologies to investigate its function typically involve:

• Comparative sequence analysis across Bacillus species

• Structural modeling based on crystal structures of related ribosomal proteins

• In vitro translation assays with and without the protein

• Interaction studies with other components of the translation machinery

What expression systems are most efficient for producing recombinant B. licheniformis rpsS protein?

Escherichia coli remains the preferred expression system for producing recombinant B. licheniformis ribosomal proteins due to its rapid growth, high cell density capabilities, and relative cost-effectiveness . When expressing B. licheniformis rpsS, consider these methodological approaches:

• Vector selection: pET series vectors are widely used for ribosomal protein expression, though they contain design flaws that can be improved upon to increase protein production .

• Host strain optimization: BL21(DE3) derivatives are commonly employed, especially those with enhanced rare codon availability.

• Induction conditions: A multivariant experimental design approach is recommended to optimize parameters:

• Codon optimization: Adjust the coding sequence to match E. coli codon usage preferences while maintaining key structural features of the mRNA.

How can multiple ribosomal binding sites enhance the expression of recombinant B. licheniformis rpsS?

Engineering mRNA leader sequences containing multiple ribosomal binding sites (RBS) has been shown to dramatically enhance translation efficiency in B. licheniformis . This innovative approach works through the following mechanism:

• Multiple RBS sequences allow translation initiation from multiple sites, increasing the probability of successful translation initiation events.

• For optimal implementation with rpsS expression:

  • Design a construct with 3-6 RBS sequences upstream of the rpsS coding region

  • Space the RBS sequences optimally (typically 8-12 nucleotides apart)

  • Ensure each RBS has the consensus sequence AGGAGG or close variants

  • Consider the incorporation of a TEV protease site to remove N-terminal extensions

Results from similar applications have shown that GFP expression with six RBSs increased up to five times compared to single RBS constructs, representing approximately 50% of total intracellular protein . When implementing this approach, remember that for intracellular proteins, the N-terminal sequences encoded by multiple RBSs might interfere with protein folding, necessitating protease cleavage sites for post-expression processing .

What experimental design approaches can optimize expression of B. licheniformis rpsS?

A statistically rigorous experimental design methodology is essential for optimizing rpsS expression. Fractional factorial design is particularly effective as it allows evaluation of multiple variables simultaneously with fewer experiments .

Implementation methodology:

• Variable selection: Identify 6-8 critical variables affecting expression:

  • Media components (carbon source, nitrogen source, trace elements)

  • Induction parameters (inducer concentration, OD at induction)

  • Growth conditions (temperature, pH, aeration)

• Design structure: Use a fractional factorial design (e.g., 2^8-4) with center point replicates to evaluate these variables with statistical rigor .

• Response variables: Measure:

  • Cell growth (OD600)

  • Biological activity of rpsS (functional assays)

  • Productivity (mg protein per liter of culture)

• Analysis approach: Apply multivariate analysis to identify statistically significant variables and their interactions.

This approach has enabled researchers to achieve high levels (250 mg/L) of soluble expression of recombinant proteins in E. coli , suggesting similar protocols could be effective for rpsS production.

What are the best purification strategies for obtaining high-purity recombinant B. licheniformis rpsS?

Purification of recombinant rpsS requires a strategic approach that preserves the protein's native structure while achieving high purity. The recommended methodological sequence is:

• Cell lysis optimization:

  • For E. coli expression systems, use sonication or high-pressure homogenization

  • Include lysozyme (1 mg/mL) in lysis buffer to enhance cell wall disruption

  • Incorporate RNase to remove bound RNA that may co-purify with ribosomal proteins

• Initial capture:

  • If expressed with a His-tag, use immobilized metal affinity chromatography (IMAC)

  • Buffer composition: 50 mM Tris-HCl (pH 8.0), 300 mM NaCl, 10 mM imidazole (binding); 250 mM imidazole (elution)

• Intermediate purification:

  • Ion exchange chromatography exploiting rpsS's basic properties (pI typically >9.5)

  • Use strong cation exchange resins (e.g., SP Sepharose)

• Polishing:

  • Size exclusion chromatography to remove aggregates and achieve >95% purity

  • Typical buffer: 20 mM HEPES (pH 7.5), 150 mM KCl, 5 mM MgCl₂

• Quality assessment:

  • SDS-PAGE for purity evaluation

  • Mass spectrometry for identity confirmation

  • Circular dichroism for secondary structure verification

This approach typically yields 10-15 mg of purified rpsS per liter of culture with >90% homogeneity.

How can researchers investigate the role of B. licheniformis rpsS in antibiotic resistance mechanisms?

B. licheniformis has shown resistance to multiple antibiotics in clinical settings , and ribosomal proteins are often implicated in resistance mechanisms. To investigate rpsS's potential role:

• Comparative sequence analysis:

  • Align rpsS sequences from resistant and susceptible strains

  • Identify mutations that correlate with resistance phenotypes

• Site-directed mutagenesis:

  • Introduce identified mutations into recombinant rpsS

  • Express mutant proteins in heterologous systems

• Functional assays:

  • In vitro translation assays with increasing antibiotic concentrations

  • Measure IC50 values for different antibiotics with wild-type vs. mutant rpsS

• Structural biology approach:

  • Determine crystal structures of wild-type and mutant rpsS

  • Map mutations to functional domains

• In vivo complementation:

  • Create rpsS deletion strains complemented with mutant alleles

  • Test antibiotic susceptibility profiles

This systematic approach can reveal whether specific mutations in rpsS contribute to the polyresistant phenotype observed in some B. licheniformis clinical isolates .

What methods are available for studying interactions between rpsS and other ribosomal components?

Understanding rpsS interactions with other ribosomal components requires sophisticated methodological approaches:

• Co-immunoprecipitation (Co-IP):

  • Express tagged rpsS in B. licheniformis

  • Isolate intact ribosomes under native conditions

  • Use tag-specific antibodies to pull down rpsS and associated proteins

  • Identify binding partners via mass spectrometry

• Surface Plasmon Resonance (SPR):

  • Immobilize purified rpsS on a sensor chip

  • Flow other ribosomal proteins or rRNA fragments over the surface

  • Measure binding kinetics (kon, koff) and affinity (KD)

• Cross-linking studies:

  • Treat intact ribosomes with chemical cross-linkers

  • Identify cross-linked peptides via mass spectrometry

  • Map interaction sites to 3D structural models

• FRET analysis:

  • Create fluorescently labeled rpsS and potential binding partners

  • Measure energy transfer to determine proximity and orientation

  • Perform in vitro assembly assays to monitor dynamics

• Cryo-electron microscopy:

  • Visualize ribosomes with and without rpsS

  • Identify structural changes and interaction networks

  • Generate 3D models at near-atomic resolution

These methods provide complementary data to build a comprehensive interaction map of rpsS within the ribosomal complex.

How can the solubility of recombinant B. licheniformis rpsS be improved during expression?

Ribosomal proteins often form inclusion bodies when overexpressed. To enhance solubility of recombinant B. licheniformis rpsS:

• Expression parameter optimization:

• Fusion tag selection:

  • SUMO tag significantly increases solubility of many ribosomal proteins

  • MBP (maltose-binding protein) enhances solubility through its chaperone-like activity

  • Thioredoxin fusion can prevent aggregation

• Co-expression with chaperones:

  • GroEL/GroES system assists protein folding

  • DnaK/DnaJ/GrpE chaperone system prevents aggregation

  • Trigger factor stabilizes nascent polypeptides

• Buffer optimization:

  • Include 5-10% glycerol as a stabilizing agent

  • Add 50-100 mM L-arginine to reduce protein-protein interactions

  • Maintain ionic strength with 100-300 mM NaCl or KCl

• Experimental design approach:

  • Apply fractional factorial design to test combinations of the above factors

  • Measure soluble protein fraction as the primary response variable

  • Iterate optimization based on statistical analysis of results

This systematic approach has been shown to increase soluble expression yield by 2-5 fold for challenging proteins .

How can rpsS from B. licheniformis be used in phylogenetic studies of Bacillus species?

The S19 ribosomal protein has been identified as having partial phylogenetic significance in Bacillus species . To effectively use rpsS in phylogenetic studies:

• Sequence acquisition protocol:

  • PCR-amplify the rpsS gene from multiple Bacillus isolates

  • Design primers targeting conserved flanking regions

  • Sequence using bidirectional Sanger sequencing for accuracy

• Multiple sequence alignment methodology:

  • Use MUSCLE or MAFFT algorithms with iterative refinement

  • Apply manual curation to ensure proper alignment of conserved motifs

  • Remove ambiguously aligned regions for phylogenetic analysis

• Phylogenetic tree construction:

  • Maximum likelihood method with appropriate evolutionary models (e.g., JTT+G)

  • Bayesian inference for posterior probability assessment

  • Bootstrapping (>1000 replicates) for branch support evaluation

• Comparative analysis with reference markers:

  • Parallel analysis with 16S rRNA sequences as the gold standard

  • Compare tree topologies using tanglegram visualization

  • Calculate congruence indices to quantify phylogenetic signal similarity

• Integrated approach for improved resolution:

  • Combine rpsS with other ribosomal proteins showing high phylogenetic significance (L6, L7/12, L9, L13, L24, L32, S3, S9, S12, S15, S16, S17, S18)

  • Generate concatenated alignments for multi-gene phylogeny

  • Apply partitioned models to accommodate different evolutionary rates

This approach leverages rpsS's evolutionary properties while acknowledging its limitations in resolving certain species relationships, particularly between B. licheniformis and B. pumilus .

What approaches can resolve contradictory data regarding rpsS function in protein translation?

Contradictory findings about rpsS function can be systematically addressed through:

• Comprehensive literature analysis:

  • Perform meta-analysis of published data

  • Identify methodological differences that might explain contradictions

  • Develop a conceptual framework integrating divergent findings

• In vitro translation system approach:

  • Reconstitute ribosomes with and without rpsS

  • Measure translation rates using reporter mRNAs

  • Assess fidelity with misincorporation assays

  • Test different environmental conditions (temperature, ionic strength)

• Single-case experimental designs (SCEDs):

  • Apply reversal design methodology where rpsS is added and removed sequentially

  • Measure translation parameters at each phase

  • Establish causal relationships through experimental control

• Cryo-EM structural analysis:

  • Capture ribosomes in different functional states

  • Compare conformational changes in the presence/absence of rpsS

  • Correlate structural observations with functional data

• Site-directed mutagenesis strategy:

  • Create a panel of point mutations in conserved residues

  • Assess the effect of each mutation on translation parameters

  • Build a functional map of critical residues

This integrated approach can help resolve contradictions by identifying condition-specific roles of rpsS and establishing when and how the protein influences translation.

How can researchers determine if post-translational modifications of B. licheniformis rpsS affect ribosome function?

Post-translational modifications (PTMs) of ribosomal proteins can significantly impact ribosome function. To investigate PTMs of B. licheniformis rpsS:

• Comprehensive PTM mapping:

  • Isolate native rpsS from B. licheniformis ribosomes

  • Analyze using high-resolution mass spectrometry

  • Apply multiple proteolytic digestions for complete sequence coverage

  • Use complementary fragmentation methods (CID, ETD, HCD)

• Site-specific mutagenesis strategy:

  • Create non-modifiable mutants (e.g., S→A for phosphorylation sites)

  • Generate phosphomimetic mutants (e.g., S→D/E)

  • Express in B. licheniformis and assess ribosome function

• In vitro modification assays:

  • Identify responsible modification enzymes

  • Reconstitute modification reactions with purified components

  • Create modified and unmodified rpsS for comparative functional studies

• Quantitative proteomics workflow:

  • Use SILAC or TMT labeling to quantify modification stoichiometry

  • Compare modification levels under different growth conditions

  • Correlate with changes in translation efficiency

• Structural biology approach:

  • Determine structures of ribosomes containing modified vs. unmodified rpsS

  • Map modifications to functional regions (e.g., mRNA or tRNA binding sites)

  • Model electrostatic and conformational changes resulting from modifications

This systematic approach can establish causal relationships between specific PTMs and functional changes in translation efficiency, accuracy, or regulation.

What are the optimal experimental designs for studying the role of rpsS in antibiotic resistance mechanisms?

To rigorously investigate rpsS's role in antibiotic resistance mechanisms, consider these experimental design approaches:

• True experimental design with randomization:

  • Randomly assign B. licheniformis cultures to control or experimental groups

  • Apply antibiotics to both groups at varying concentrations

  • Compare survival rates and growth kinetics

  • Isolate survivors and sequence rpsS to identify potential mutations

• Factorial experimental design:

  • Test multiple antibiotics simultaneously (e.g., aminoglycosides, macrolides)

  • Include rpsS variants as an experimental factor

  • Analyze main effects and interactions between antibiotics and rpsS mutations

  • Establish dose-response relationships for each condition

• Reversal design strategy:

  • Create conditional expression systems for wild-type and mutant rpsS

  • Observe translation parameters during alternating phases of expression

  • Establish causal relationships through multiple reversals

  • This single-case experimental design provides strong internal validity

• Combined multiple baseline/reversal design:

  • Measure multiple translation parameters simultaneously (rate, fidelity, termination)

  • Introduce rpsS mutations sequentially

  • Apply antibiotic pressure at controlled time points

  • Analyze temporal relationships between mutations, resistance, and translation metrics

• Adaptive experimental design:

  • Begin with broad screening of conditions

  • Use initial results to narrow focus to significant variables

  • Increase replication for promising conditions

  • Apply Bayesian optimization to efficiently explore parameter space