Recombinant Rhodopirellula baltica Ribonuclease H (rnhA)

What is the functional role of Ribonuclease H (rnhA) in Rhodopirellula baltica?

Ribonuclease H (rnhA) in R. baltica, similar to other bacterial RNase H enzymes, plays a critical role in nucleic acid metabolism by catalyzing the degradation of RNA in RNA-DNA hybrids. Based on studies in other bacterial systems, particularly E. coli, we understand that RNase H is essential for:

• Removing RNA primers during DNA replication

• Resolving R-loop structures that form during transcription

• Maintaining genome stability by preventing accumulation of ribonucleotides in DNA

• Supporting proper cell cycle progression

R. baltica, as a representative of the globally distributed phylum Planctomycetes, exhibits unique cellular characteristics and genome arrangements that may influence rnhA function in ways still being investigated .

How does R. baltica rnhA gene expression change throughout its growth cycle?

Transcriptional profiling of R. baltica shows distinct patterns of gene regulation across different growth phases. While the search results don't specifically highlight rnhA expression patterns, we can infer from general transcription patterns that:

• During exponential growth phase, only approximately 2% of genes show differential regulation, reflecting favorable nutritional conditions

• The transition from exponential to stationary phase shows increased gene regulation

• Late stationary phase exhibits significant downregulation of genes involved in DNA replication, recombination, and ribosomal machinery

This suggests rnhA expression may follow patterns similar to other DNA replication and repair genes, likely with higher expression during active growth and reduced expression during stationary phase.

What genetic elements regulate rnhA expression in R. baltica?

The regulation of rnhA in R. baltica likely involves multiple factors:

• Transcription regulators that respond to growth phase transitions (numerous genes for transcription regulation show differential expression during growth phases)

• Stress-responsive elements, as R. baltica demonstrates significant transcriptional changes under stress conditions

• DNA topology and genome organization factors, particularly relevant given R. baltica's limited operon structures

During late stationary phase, R. baltica increases expression of transposases, integrases, and recombinases, suggesting genome rearrangements that may affect gene regulation broadly, potentially including rnhA .

How does the absence of rnhA affect genome stability in R. baltica compared to E. coli models?

Studies in E. coli demonstrate that RNase H deficiency creates significant genome instability. The effects in R. baltica may be similar but with organism-specific variations:

• E. coli rnhA mutants accumulate R-loops and R-lesions (ribonucleotide-containing DNA lesions)

• Double mutants lacking both RNase HI and HII (rnhAB) in E. coli show severe phenotypes including:

  • Reduced viability (up to 100-fold decrease when translation is inhibited)

  • Abnormal cell morphology (filamentation with abnormal nucleoids)

  • SOS response induction

  • Chromosome fragmentation

R. baltica, with its unique cell biology and life cycle, may exhibit distinct manifestation of these genome stability issues. The significantly different genome structure of R. baltica, with its limited operon organization, suggests potentially heightened sensitivity to R-loop formation and accumulation .

What experimental approaches effectively measure R-loop formation in R. baltica?

Based on methodologies used in other bacterial systems, effective approaches for studying R-loops in R. baltica include:

Molecular Detection Techniques:

• DNA-RNA immunoprecipitation using S9.6 antibody specific for RNA-DNA hybrids

• In vivo crosslinking followed by immunoprecipitation

• Electron microscopy to visualize R-loop structures

• Plasmid relaxation assays that detect R-tracts and R-patches

Genetic Approaches:

• Construction of rnhA knockout strains

• RNase H overexpression studies

• Translation inhibition experiments, which exacerbate R-loop-related phenotypes in RNase H-deficient E. coli

Chromosome Integrity Assessment:

• Pulse-field gel electrophoresis to detect chromosome fragmentation

• Fluorescence microscopy to observe nucleoid morphology

E. coli studies demonstrate that translation inhibition significantly decreases viability in rnhAB mutants (15-100 fold) and causes chromosome fragmentation, providing a potential experimental approach for R. baltica .

How can multisite λ dynamics (MSλD) be applied to engineer stabilized variants of R. baltica RNase H?

Based on recent advances in computational protein design, MSλD offers a rigorous free energy calculation method that can be applied to R. baltica RNase H:

Implementation Strategy:

• Generate a structural model of R. baltica RNase H through crystallography, NMR, or homology modeling

• Identify key positions for mutation based on sequence conservation analysis and structural assessment

• Apply MSλD to calculate stability changes for all possible variants in the sequence space

• Experimentally validate a subset of variants to confirm predictions

Expected Performance:
Recent application of MSλD to E. coli RNase H achieved remarkable accuracy with:

• Pearson correlation of 0.86 between predicted and measured stabilities

• Root mean squared error of 1.18 kcal/mol

• Ability to screen thousands of potential variants computationally

This approach could identify stabilizing mutations with minimal sequence changes. For example, in the E. coli study, researchers designed a chimera with stability comparable to a consensus ancestral sequence but requiring only half the mutations .

What are the consequences of R-lesion formation in R. baltica and how do they progress to DNA damage?

Based on E. coli studies, R-lesions (ribonucleotide-containing DNA lesions) in R. baltica likely follow a progression:

R-lesion Formation and Progression:

• R-loops form during transcription, particularly when transcription and translation coupling is disrupted

• These may be converted to R-tracts (contiguous runs of ≥4 RNA nucleotides within DNA)

• R-tracts progress to R-gaps (single-strand gaps containing ribonucleotides)

• Finally, R-gaps can lead to double-strand breaks

Detection Approaches:

• Plasmid relaxation tests can detect R-patches (1-3 ribonucleotides) in DNA

• R-gaps can be detected in chromosomal DNA through specialized assays

• Double-strand breaks can be visualized through pulse-field gel electrophoresis

Interestingly, E. coli studies failed to detect accumulated R-tracts in rnhAB mutants despite their theoretical formation, suggesting rapid progression to more serious lesions—explaining why double-strand breaks accumulate while R-tracts do not .

How does the regulatory network around rnhA in R. baltica respond to environmental stressors?

R. baltica demonstrates complex transcriptional responses to environmental changes:

Observed Regulatory Patterns:

• Under nutrient limitation, R. baltica increases glutamate dehydrogenase levels, adapting cell wall composition

• Stress conditions trigger expression of genes coding for transposases, integrases, and recombinases

• Specific genes activated under stress (examples from R. baltica data):

This suggests that rnhA regulation in R. baltica likely integrates into broader stress response networks, potentially with condition-specific expression patterns that help manage increased R-loop formation during stress.

What protocol optimizations are recommended for heterologous expression of R. baltica RNase H?

For optimal heterologous expression of R. baltica RNase H:

Expression System Selection:

• E. coli BL21(DE3) or Rosetta strains for handling rare codons

• Consider codon optimization for R. baltica genes, which have different GC content

• Test multiple expression vectors with different promoter strengths

Expression Conditions:

• Perform temperature optimization (15-30°C) with lower temperatures often favoring proper folding

• Test induction concentrations (0.1-1.0 mM IPTG) and duration (4-16 hours)

• Consider auto-induction media for higher yields

Purification Strategy:

• N-terminal or C-terminal His-tag fusion for IMAC purification

• Ion exchange chromatography as a secondary purification step

• Size exclusion for final polishing and buffer exchange

Activity Assessment:

• Fluorescence-based assays using labeled RNA-DNA hybrids

• Gel-based activity assays monitoring degradation of RNA in RNA-DNA substrates

The unusual cell biology of R. baltica may necessitate special considerations for functional expression of its proteins .

How can transcriptomic analysis be designed to identify the rnhA regulon in R. baltica?

A comprehensive transcriptomic approach would include:

Experimental Design:

• Generate rnhA knockout or conditional expression strains

• Sample across multiple growth phases, as R. baltica gene expression varies significantly by phase

• Include stress conditions known to affect R-loop formation

• Minimum biological triplicates for statistical power

RNA-Seq Methodology:

• Strand-specific library preparation to detect antisense transcription

• rRNA depletion rather than poly-A selection given bacterial samples

• Deep sequencing (>20M reads per sample) to capture low-abundance transcripts

Data Analysis Strategy:

• Differential expression analysis comparing wild-type vs. rnhA mutant

• Time-course analysis across growth phases

• Network analysis to identify co-regulated gene clusters

• Integration with existing R. baltica transcriptome data

Expected Outcomes:

• Identification of genes directly and indirectly affected by rnhA deficiency

• Discovery of regulatory networks connected to rnhA function

• Understanding of condition-specific dependencies on rnhA

The analysis should account for R. baltica's complex growth patterns, as only 2% of genes show regulation during exponential phase, while 12% show regulation in late stationary phase .

Source: Transcriptional profiling of R. baltica

What approaches can distinguish between R-loops and other ribonucleotide-containing DNA lesions in R. baltica?

Distinguishing between different types of ribonucleotide-containing DNA structures requires specialized techniques:

R-loop Detection:

• S9.6 antibody-based immunoprecipitation (DRIP)

• In vitro sensitivity to RNase H treatment

• Bisulfite sequencing that detects single-stranded DNA in R-loops

R-tract Detection (≥4 consecutive ribonucleotides):

• Alkaline gel electrophoresis (R-tracts are alkali-sensitive)

• Selective degradation with RNase HI (cleaves R-tracts but not R-patches)

• Mass spectrometry analysis of nucleic acid composition

R-patch Detection (1-3 ribonucleotides):

• Plasmid relaxation tests (RNase HII introduces nicks at RNA-DNA junctions)

• Ribonucleotide excision repair (RER) pathway substrates

• Nick translation assays

R-gap Detection:

• Specialized assays for single-strand gaps containing ribonucleotides

• Two-dimensional gel electrophoresis

• Electron microscopy visualization

E. coli studies demonstrated that plasmid relaxation tests could detect R-patches but not R-tracts in rnhAB mutants, while specialized assays detected R-gaps in chromosomal DNA .

How do translation inhibition experiments reveal insights into R-loop dynamics in R. baltica?

Based on E. coli findings, translation inhibition experiments can provide critical insights:

Experimental Approach:

• Treat cultures with translation inhibitors (chloramphenicol, tetracycline, or linezolid)

• Assess viability through CFU counts

• Analyze chromosome integrity via pulse-field gel electrophoresis

• Compare effects between wild-type, rnhA single mutant, and rnhAB double mutant strains

Expected Results Based on E. coli Studies:

• Wild-type cells show minimal effects

• rnhA single mutants show moderate sensitivity

• rnhAB double mutants show dramatic loss of viability (15-100 fold decrease) and chromosome fragmentation

Mechanistic Insights:

• Translation inhibition increases R-loop formation by leaving nascent mRNA unprotected

• In RNase H-deficient cells, these R-loops progress to R-lesions and DNA damage

• The differential sensitivity between single and double mutants reveals the critical backup role of RNase HII

This approach demonstrates that R-loops contribute to R-lesion formation but are not the lesions themselves—otherwise both rnhA and rnhAB mutants would be equally sensitive to translation inhibition .

What sequence features distinguish R. baltica RNase H from other bacterial homologs?

Computational sequence analysis of R. baltica RNase H would focus on:

Primary Structure Analysis:

• Multiple sequence alignment with diverse bacterial RNase H sequences

• Identification of conserved catalytic residues

• Planctomycetes-specific sequence features

• Assessment of substrate-binding regions

Evolutionary Analysis:

• Phylogenetic tree construction to place R. baltica RNase H in evolutionary context

• Calculation of selection pressure on different regions of the protein

• Identification of positions under positive or purifying selection

Structural Prediction:

• Secondary structure prediction

• Homology modeling based on resolved RNase H structures

• Active site configuration analysis

• Surface electrostatics comparison with other bacterial homologs

The application of methods similar to those used for E. coli RNase H evolution studies could reveal how R. baltica's unique marine lifestyle and cell biology have shaped its RNase H structure and function .

How can sequence-stability relationships in R. baltica RNase H be predicted computationally?

Based on approaches described for E. coli RNase H, computational prediction of R. baltica RNase H stability would involve:

MSλD Implementation:

• Generate a structural model of R. baltica RNase H

• Identify positions of interest based on conservation analysis

• Define sequence space to examine (e.g., chimeras between R. baltica and consensus sequences)

• Perform rigorous free energy calculations to predict stability changes

Performance Metrics:
The E. coli study demonstrated impressive accuracy metrics that could be achievable for R. baltica RNase H:

• Pearson correlation of 0.86 between computed and measured stabilities

• Root mean squared error of 1.18 kcal/mol

Applications:

• Identify minimal mutations needed to enhance stability

• Design thermostable variants for biotechnological applications

• Understand evolutionary constraints on RNase H sequence

Validation Strategy:

• Experimental testing of predicted most stable and least stable variants

• Chemical denaturation experiments to measure ΔG of unfolding

• Correlation of stability with enzymatic activity

What structural adaptations might R. baltica RNase H exhibit for function in marine environments?

Marine organisms often have structural adaptations to their environment that might be present in R. baltica RNase H:

Potential Adaptations:

• Increased surface negative charge to function in higher salt concentrations

• Modified flexibility in substrate binding regions

• Altered catalytic residue pKa values for function at different pH ranges

• Structural features that confer halotolerance

Investigation Approaches:

• Comparative structural analysis with non-marine bacterial RNase H enzymes

• Molecular dynamics simulations at varying salt concentrations

• Hydrogen-deuterium exchange mass spectrometry to assess conformational dynamics

• Activity assays under varying salt and pH conditions

R. baltica has been observed to exhibit salt resistance , suggesting its proteins, including RNase H, may have adaptations for function in marine conditions.

How do R-loop structures in R. baltica differ from those in model organisms like E. coli?

R-loop structures in R. baltica might exhibit unique characteristics related to its distinctive genome organization:

Potential Differences:

• Distribution patterns related to R. baltica's limited operon structures

• Association with the unique membrane-bound nucleoid organization in Planctomycetes

• Different strand bias patterns related to R. baltica's GC content

• Unique R-loop hotspots associated with marine adaptations

Investigation Methods:

• Genome-wide R-loop mapping techniques (DRIP-seq)

• Comparative R-loop profiling between R. baltica and model organisms

• Correlation of R-loop distribution with transcriptional activity

• Analysis of sequence features associated with R-loop formation sites

The absence of typical bacterial operon structures in R. baltica may necessitate genome rearrangements under stress conditions , potentially creating unique patterns of R-loop formation and resolution.