Recombinant Rhodopirellula baltica 50S ribosomal protein L6 (rplF)

What is the structural and functional role of 50S ribosomal protein L6 (rplF) in Rhodopirellula baltica?

The 50S ribosomal protein L6 (rplF) is a critical component of the large ribosomal subunit in Rhodopirellula baltica. Similar to its counterparts in other bacterial species, it binds to the 23S rRNA and plays an important role in maintaining the secondary structure of this RNA molecule. The protein is strategically located near the subunit interface in the base of the L7/L12 stalk and in proximity to the tRNA binding site of the peptidyltransferase center, indicating its role in translation processes . This positioning suggests that rplF contributes to both the structural integrity of the ribosome and its functional capacity during protein synthesis.

Functionally, rplF belongs to the universal ribosomal protein uL6 family, which is highly conserved across diverse bacterial species . In the context of Rhodopirellula baltica's unique cellular compartmentalization (riboplasma and paryphoplasm), the protein likely plays a role in the specialized protein synthesis machinery of this planctomycete.

How does Rhodopirellula baltica rplF gene expression respond to environmental stressors?

Rhodopirellula baltica demonstrates significant transcriptional responses to environmental stressors, with ribosomal proteins often serving as stress sensors. While specific data on rplF regulation is limited, research on R. baltica's stress response provides valuable context. Under various stress conditions (heat, cold, and salinity), approximately 55% of genes associated with the ribosomal machinery are down-regulated . This includes 18 genes encoding proteins of both small and large ribosomal subunits.

During heat shock and high salinity conditions, ribosomal proteins are permanently repressed, whereas under cold shock they are only repressed within the first hour, followed by up-regulation after 300 minutes . This pattern suggests that rplF, as part of the ribosomal machinery, may follow similar expression patterns during environmental stress. The temporary down-regulation likely represents an energy conservation strategy, followed by adaptive recovery during prolonged exposure to cold conditions.

What expression systems are most suitable for recombinant production of Rhodopirellula baltica rplF?

For recombinant production of Rhodopirellula baltica rplF, E. coli-based expression systems remain the most widely used platform due to their efficiency and scalability. Several factors must be considered when selecting an appropriate expression system:

For most research applications, the pET system with an N-terminal 6xHis-tag facilitates efficient purification while minimizing interference with protein function. Codon optimization is recommended as planctomycete codon usage differs from E. coli, which can significantly improve expression yields. The protein synthesis can be achieved at a cost starting at approximately $99 plus $0.30 per amino acid, with production timelines as short as two weeks .

How can structural studies of rplF contribute to our understanding of Planctomycete ribosomal evolution?

Structural analysis of Rhodopirellula baltica rplF offers a unique window into the evolutionary adaptations of the planctomycete ribosomal machinery. Planctomycetes represent a distinct bacterial lineage with unique cellular compartmentalization, and their ribosomal components may reflect specialized adaptations.

Methodological approach for structural evolutionary studies:

• Comparative structural analysis through X-ray crystallography or cryo-EM of R. baltica rplF in complex with 23S rRNA fragments

• Identification of planctomycete-specific structural motifs through alignment with other bacterial rplF structures

• Functional validation of unique structural elements through site-directed mutagenesis

• Reconstruction of evolutionary relationships based on structural conservation

The 177-amino acid sequence can be analyzed to identify conserved domains versus planctomycete-specific regions . Particular attention should be paid to regions that interact with the 23S rRNA and neighboring ribosomal proteins, as these may reveal adaptations specific to Planctomycete cellular architecture.

How does rplF expression correlate with Rhodopirellula baltica's cellular compartmentalization during protein translocation?

R. baltica's cellular compartmentalization presents unique challenges and opportunities for understanding ribosomal protein function in the context of intracellular organization. During salt stress, R. baltica activates protein translocation systems, as evidenced by the induction of SecA (RB11690) belonging to the Sec system, which facilitates protein movement from the riboplasma to the paryphoplasm or medium .

To investigate rplF's role in this context, researchers should:

• Use fluorescently tagged rplF to track its localization during various cellular states

• Employ proximity labeling techniques (BioID or APEX) to identify rplF interaction partners in different cellular compartments

• Perform ribosome profiling experiments under conditions that alter compartmentalization

• Correlate rplF expression with SecA and other translocation machinery components

This approach would clarify whether rplF plays a specialized role in the compartmentalized translation system of R. baltica, potentially revealing novel functions beyond those seen in non-compartmentalized bacteria.

What computational models best predict rplF-23S rRNA interactions in Rhodopirellula baltica?

Advanced computational modeling of rplF-23S rRNA interactions requires specialized approaches that account for the unique features of planctomycete ribosomes. A recommended methodological workflow includes:

• Homology modeling: Using the known crystal structures of rplF from related organisms as templates, constructing a three-dimensional model of R. baltica rplF

• RNA structure prediction: Generating secondary and tertiary structure models of the 23S rRNA regions that interact with rplF

• Molecular docking simulations: Employing software such as HADDOCK or RNP-Dock that specialize in protein-RNA interactions

• Molecular dynamics simulations: Performing extended simulations (>100 ns) to observe the stability and dynamics of the predicted interactions

• Energy minimization and binding affinity calculations: Quantifying the strength of interactions to identify key binding residues

The computational predictions should be validated experimentally through techniques such as RNA footprinting, SHAPE analysis, or crosslinking studies. This integrated approach provides a robust framework for understanding the molecular basis of rplF function in R. baltica ribosomes.

What are the optimal conditions for heterologous expression of Rhodopirellula baltica rplF?

Optimizing heterologous expression of R. baltica rplF requires systematic evaluation of multiple parameters. Based on empirical data from related ribosomal proteins, the following protocol is recommended:

Expression vector selection:

• pET28a(+) with N-terminal 6xHis-tag for general applications

• pMAL-c2X with MBP fusion for improving solubility if inclusion body formation occurs

Host strain optimization:

Expression conditions:

• Culture in LB medium to OD600 of 0.6-0.8

• Induce with 0.5 mM IPTG

• Express at 18°C for 16-18 hours to maximize soluble protein yield

• Harvest cells by centrifugation at 4,000×g for 15 minutes

This approach typically yields 10-15 mg of purifiable protein per liter of culture. For structural studies requiring isotopic labeling, minimal media with 15N-ammonium chloride and/or 13C-glucose should be employed.

How can site-directed mutagenesis be used to investigate functional domains of rplF?

Site-directed mutagenesis offers a powerful approach to dissect the functional architecture of rplF. Based on the amino acid sequence of related rplF proteins, several key regions warrant targeted investigation :

Recommended mutagenesis strategy:

• RNA-binding domain analysis:

  • Identify positively charged residues (Lys, Arg) likely involved in RNA binding

  • Generate alanine substitutions using overlap extension PCR

  • Test mutants for 23S rRNA binding using electrophoretic mobility shift assays

• Interface interaction studies:

  • Target residues at the L7/L12 stalk interface

  • Create conservative (similar amino acid) and non-conservative mutations

  • Assess impact on ribosome assembly using sucrose gradient ultracentrifugation

• Functional validation:

  • Employ in vitro translation assays with purified components

  • Measure peptidyltransferase activity with fluorescently labeled substrates

  • Correlate structural perturbations with functional outcomes

A systematic mutational analysis should proceed from single amino acid changes to more complex alterations of structural motifs. Each mutant should be characterized through a combination of biochemical (binding assays), biophysical (circular dichroism, thermal shift), and functional (translation activity) approaches to generate a comprehensive structure-function map.

How can multilocus sequence analysis (MLSA) be used to study rplF evolution across Rhodopirellula species?

Multilocus sequence analysis (MLSA) provides a robust framework for studying the evolution of rplF across Rhodopirellula species. Based on established MLSA methodologies for Rhodopirellula , the following approach is recommended:

• Gene selection: Include rplF alongside established housekeeping genes used in Rhodopirellula MLSA (acsA, guaA, trpE, purH, glpF, fumC, icd, glyA, and mdh)

• Primer design for rplF amplification:

  • Design primers in conserved regions flanking variable domains

  • Verify primer specificity against Rhodopirellula and related genomes

  • Optimize PCR conditions through gradient PCR (recommended annealing temperature: 60°C)

• Sequencing and analysis workflow:

  • Amplify target genes using optimized PCR conditions

  • Purify amplicons through size exclusion chromatography

  • Sequence using Applied Biosystems technology

  • Manually examine sequences and assemble with reference to type strain sequences

  • Align sequences using ClustalW in ARB software package

  • Generate phylogenetic trees using maximum likelihood, maximum parsimony, and neighbor joining methods

• Evolutionary analysis:

  • Calculate similarity matrices at the nucleotide level

  • Identify operational taxonomic units (OTUs) based on sequence similarity

  • Compare rplF phylogeny with other housekeeping genes to detect horizontal gene transfer

This approach has successfully identified 13 genetically defined OTUs in previous Rhodopirellula studies and can be adapted to specifically track rplF evolution within the genus.

How can protein aggregation issues be addressed when expressing recombinant rplF?

Protein aggregation is a common challenge when expressing recombinant ribosomal proteins, including rplF. A systematic troubleshooting approach should include:

Prevention strategies:

• Lower expression temperature to 15-18°C

• Reduce inducer concentration (0.1-0.2 mM IPTG)

• Co-express with molecular chaperones (GroEL/GroES, DnaK/DnaJ/GrpE)

• Include solubilizing agents in lysis buffer (0.1% Triton X-100, 1M urea)

Solubilization methods for inclusion bodies:

Analytical assessment:

• Dynamic light scattering to monitor aggregation state

• Size-exclusion chromatography to quantify monomer/oligomer distribution

• Thermal shift assays to assess stability of refolded protein

When aggregation persists, fusion to solubility-enhancing tags like MBP, SUMO, or Fh8 often proves effective. Tag removal should be performed after initial purification steps to prevent reaggregation.

How are rplF expression patterns affected by stress response in Rhodopirellula baltica?

Understanding rplF expression patterns during stress response requires integration with broader ribosomal protein regulation in R. baltica. Research has shown that under environmental stress, approximately 55% of R. baltica's ribosomal machinery genes are down-regulated . While specific data on rplF is not available, its behavior can be inferred from the general pattern:

Stress-responsive expression patterns:

• During heat shock and high salinity: permanent repression of ribosomal genes

• During cold shock: temporary repression (first hour) followed by up-regulation at 300 minutes

To specifically study rplF expression under stress conditions, the following methodological approach is recommended:

• RT-qPCR analysis:

  • Design primers specific to R. baltica rplF

  • Normalize expression to stable reference genes (validated under stress conditions)

  • Monitor expression at multiple time points (20, 40, 60, 300 minutes) after stress induction

• Ribosome profiling:

  • Generate ribosome footprint libraries under various stress conditions

  • Analyze translational efficiency of rplF relative to other ribosomal proteins

  • Correlate with global changes in translation machinery

• Correlation with stress-responsive regulators:

  • Monitor expression relative to known stress regulators like ECF sigma factors

  • R. baltica contains 37 genes belonging to the extracytoplasmic function subfamily of sigma 70

  • Focus on relationships with up-regulated ECF factors (RB138, RB13241, RB10049) that respond to all stress conditions

This integrated approach would position rplF expression within R. baltica's broader stress response network, providing insights into its regulation and potential specialized roles during environmental adaptation.

What analytical techniques best verify the structural integrity of purified recombinant rplF?

Verifying the structural integrity of purified recombinant rplF requires a multi-technique approach that assesses both primary structure and higher-order folding:

Primary structure verification:

• Mass spectrometry analysis:

  • MALDI-TOF to confirm the expected mass of 19.4 kDa

  • LC-MS/MS peptide mapping for sequence coverage

  • Top-down proteomics for intact protein analysis

Secondary and tertiary structure assessment:

• Circular dichroism (CD) spectroscopy:

  • Far-UV (190-260 nm) to determine secondary structure composition

  • Near-UV (250-350 nm) to assess tertiary structure organization

  • Thermal denaturation to determine stability (melting temperature)

• Fluorescence spectroscopy:

  • Intrinsic tryptophan fluorescence to monitor folding state

  • ANS binding to detect exposed hydrophobic patches

Functional validation:

• RNA binding assays:

  • Electrophoretic mobility shift assay (EMSA) with 23S rRNA fragments

  • Filter binding assays to determine binding constants

  • Microscale thermophoresis for quantitative binding analysis

Comprehensive quality assessment workflow:

• Verify molecular weight via SDS-PAGE and mass spectrometry

• Confirm amino acid composition through amino acid analysis

• Assess secondary structure content by CD spectroscopy

• Determine thermal stability through differential scanning fluorimetry

• Validate functional activity through RNA binding assays

This multi-parameter analysis ensures that the recombinant protein not only has the correct primary sequence but also maintains the structural features necessary for biological activity.

How can cryo-electron microscopy advance our understanding of rplF's role in Rhodopirellula baltica ribosomes?

Cryo-electron microscopy (cryo-EM) offers unprecedented opportunities to study the structure and function of R. baltica ribosomes and the specific role of rplF. A methodological approach for such studies would include:

• Sample preparation optimization:

  • Isolation of intact 70S ribosomes from R. baltica cultures

  • Preparation of reconstituted ribosomes with labeled recombinant rplF

  • Vitrification optimization for planctomycete ribosomes

• Data collection strategy:

  • High-resolution imaging (300kV microscope with direct electron detector)

  • Collection of multiple datasets in different functional states

  • Focused classification on the L6 region to resolve conformational heterogeneity

• Structural analysis pipeline:

  • Single particle analysis using RELION or cryoSPARC

  • Multi-body refinement to capture dynamic regions

  • Model building and refinement against the cryo-EM density

This approach would reveal the detailed architecture of R. baltica ribosomes and the specific interactions of rplF with surrounding components, potentially uncovering adaptations unique to planctomycete translation machinery.

What role might rplF play in R. baltica's adaptation to different ecological niches?

Investigation of rplF's role in ecological adaptation requires integration of genomics, transcriptomics, and experimental approaches. The unique biogeography of Rhodopirellula species, with distinct genotypes covering different European sea regions , suggests potential adaptation of core cellular machinery to specific environmental conditions.

Research methodology:

• Comparative genomics of rplF across Rhodopirellula isolates:

  • Analyze sequence variation in rplF across the 13 genetically defined operational taxonomic units (OTUs)

  • Correlate sequence variations with geographical and environmental parameters

  • Identify potential adaptive mutations through selection pressure analysis

• Transcriptomic profiling:

  • Compare rplF expression levels across strains from different habitats

  • Analyze expression under habitat-specific stress conditions

  • Identify co-expressed genes that may form functional networks

• Experimental validation:

  • Generate recombinant rplF variants from different ecotypes

  • Compare biochemical properties (stability, RNA binding, etc.)

  • Perform complementation studies in model organisms