Recombinant Bacillus licheniformis DNA replication and repair protein recF (recF)

What is the role of RecF in Bacillus licheniformis DNA repair mechanisms?

RecF in B. licheniformis functions as a critical component in the RecFOR pathway for DNA repair, particularly in homologous recombination (HR) processes. This protein assists in loading RecA onto single-stranded DNA (ssDNA) gaps coated with single-strand binding proteins. B. licheniformis relies heavily on HR systems to repair double-strand breaks (DSBs) using homologous sequences as templates . The RecF pathway becomes especially important when replication forks encounter DNA damage, creating single-stranded gaps that require repair.

Unlike some bacteria that possess both RecBCD and RecFOR pathways, B. licheniformis appears to depend more significantly on the RecFOR pathway for certain types of DNA repair. The protein functions in coordination with RecO and RecR to form a complex that facilitates RecA-mediated strand exchange, which is fundamental to maintaining genomic integrity during environmental stress conditions.

How does RecF expression differ across growth phases in B. licheniformis?

RecF expression in B. licheniformis varies across growth phases, with expression patterns linked to the cell's DNA replication status. During exponential growth phases when DNA replication is active, RecF expression tends to increase to support repair of replication-associated damage. Expression can be regulated by various promoter systems, similar to how recombinase expression is controlled in B. licheniformis using inducible promoters like the rhamnose-inducible promoter (Prha) .

The specific timing of RecF expression is critical for effective DNA repair function. Studies with other recombination enzymes in B. licheniformis have shown that "induction time and concentration of rhamnose, along with the generation time of the strain, significantly influenced the editing efficiency" . This principle likely applies to RecF expression as well, suggesting that optimal RecF activity requires precise temporal control relative to the cell cycle and growth phase.

What structural domains characterize RecF in B. licheniformis, and how do they compare to other bacterial RecF proteins?

RecF in B. licheniformis contains several conserved structural domains typical of bacterial RecF proteins:

• ATP-binding domain: This domain belongs to the ATP-binding cassette (ABC) superfamily and enables ATP hydrolysis, which powers conformational changes necessary for RecF function.

• DNA-binding domain: This region recognizes and binds specifically to DNA structures at single-strand/double-strand junctions, a common site for DNA repair events.

• Protein-protein interaction interfaces: These domains facilitate interactions with RecO and RecR to form the functional RecFOR complex.

How does RecF contribute to homologous recombination efficiency in B. licheniformis compared to other recombination proteins?

RecF's contribution to homologous recombination (HR) efficiency in B. licheniformis must be understood in the context of this organism's generally low recombination efficiency. B. licheniformis exhibits "extremely low transformation and homologous recombination (HR) efficiency" compared to other bacterial species such as E. coli and B. subtilis . The efficiency of HR is often the rate-limiting step for genome editing in this organism.

Unlike phage-derived recombinases such as RecT, which can enhance recombination efficiency by up to 105-fold when overexpressed , native RecF typically provides more modest enhancements to HR efficiency. This difference highlights the potential advantage of phage-derived recombination systems for biotechnological applications in B. licheniformis.

What are the effects of recF gene deletion on genome stability and DNA repair capacity in B. licheniformis?

Deletion of the recF gene in B. licheniformis would likely produce several phenotypic consequences:

The effects of recF deletion would generally be less severe than recA deletion, as RecA represents the central recombinase in bacterial DNA repair systems.

How can RecF activity be modulated to enhance genome editing applications in B. licheniformis?

Several strategic approaches can be employed to modulate RecF activity for enhanced genome editing in B. licheniformis:

• Optimized expression systems: Development of tightly regulated expression systems similar to the rhamnose-inducible promoter (Prha) used for other recombinases . This promoter has demonstrated effectiveness in B. licheniformis with "the recombination efficiency reaching an impressive 16.67%" under optimal conditions .

• Protein engineering: Structure-guided modifications to enhance RecF's DNA binding affinity or interaction with RecO and RecR could increase its activity.

• Timing optimization: Since "the induction time and concentration of rhamnose, along with the generation time of the strain, significantly influenced the editing efficiency" , similar optimization of RecF expression timing relative to the cell cycle could enhance its effectiveness.

• Co-expression strategies: Coordinated expression of the complete RecFOR complex might prove more effective than modulating RecF alone.

• Hybrid systems: Creating chimeric proteins combining RecF domains with domains from more efficient recombinases might enhance activity.

These approaches would need experimental validation, with careful consideration of the specific characteristics of B. licheniformis, including its generally low transformation and recombination efficiency compared to other bacterial species .

What expression systems are most effective for producing recombinant RecF in B. licheniformis?

Based on successful expression systems developed for B. licheniformis, the following approaches are recommended for recombinant RecF production:

The rhamnose-inducible promoter (Prha) system has demonstrated particular promise in B. licheniformis, as it is "tightly regulated in the absence of rhamnose, preventing background expression" while efficiently driving gene expression upon induction . For optimal results, expression conditions should be systematically optimized with respect to:

• Induction timing relative to growth phase

• Inducer concentration

• Post-induction cultivation time

• Medium composition

The optimal conditions established for other recombinases in B. licheniformis include "induction with 1.5% rhamnose for 8 h" followed by "further culture for an additional 24 h, equivalent to approximately three generations" . Similar optimization would be necessary for recombinant RecF expression.

What methodologies are most reliable for characterizing RecF-protein interactions in B. licheniformis?

Several complementary methodologies can be employed to characterize RecF interactions with other proteins in B. licheniformis:

• Affinity purification coupled with mass spectrometry (AP-MS):

  • Tag RecF with an affinity tag (His, FLAG, etc.)

  • Purify protein complexes under native conditions

  • Identify interacting partners via mass spectrometry

  • Advantage: Captures physiological interactions in the bacterial cellular context

• Bacterial two-hybrid (B2H) analysis:

  • Test specific protein-protein interactions

  • Adaptable to high-throughput screening

  • Can validate interactions identified through AP-MS

• Surface plasmon resonance (SPR):

  • Determine binding kinetics and affinity constants

  • Requires purified recombinant proteins

  • Provides quantitative data on interaction dynamics

• Fluorescence resonance energy transfer (FRET):

  • Tag RecF and potential partners with fluorescent proteins

  • Monitor interactions in living cells

  • Spatial and temporal resolution of interactions

• Co-immunoprecipitation (Co-IP):

  • Verify interactions under various cellular conditions

  • Can be performed after different DNA damaging treatments

  • Requires development of specific antibodies or epitope tags

These methods should be adapted to account for B. licheniformis-specific factors such as cell wall composition, which may require optimization of lysis conditions and buffer compositions for efficient protein extraction.

What assays best measure RecF-mediated DNA repair activity in B. licheniformis?

Several complementary assays can effectively measure RecF-mediated DNA repair activity in B. licheniformis:

• DNA damage sensitivity assays:

  • Expose wild-type and recF mutant strains to DNA-damaging agents (UV, mitomycin C)

  • Quantify survival rates using colony-forming unit (CFU) counts

  • Generate survival curves across different damage doses

  • Similar approaches revealed UV-hypersensitivity in recA mutants

• Single-strand gap repair assays:

  • Transform cells with gapped plasmid DNA

  • Measure repair efficiency through plasmid recovery

  • Compare repair rates between wild-type and recF mutants

• Recombination frequency measurements:

  • Use selectable marker integration assays

  • Quantify recombination events at different genomic loci

  • Compare with other recombination systems where "recombination efficiency reached an impressive 16.67%" under optimal conditions

• In vitro DNA binding and ATPase assays:

  • Purify recombinant RecF protein

  • Measure DNA binding using electrophoretic mobility shift assays (EMSA)

  • Assess ATP hydrolysis rates with different DNA substrates

• Live-cell imaging of DNA repair:

  • Create fluorescently tagged RecF

  • Track recruitment to DNA damage sites

  • Measure repair kinetics in real-time

These assays should be performed under standardized conditions to enable comparison between different B. licheniformis strains, which can vary in their genetic content as revealed by genomic analysis .

How should researchers analyze variability in RecF activity across different B. licheniformis strains?

When analyzing variability in RecF activity across different B. licheniformis strains, researchers should employ the following analytical approach:

• Establish a standardized activity measurement:

  • Define clear metrics for RecF activity (e.g., DNA repair efficiency, protein-protein interaction strength)

  • Use identical experimental conditions across strains

  • Include appropriate controls in each experiment

• Correlate activity with genomic features:

  • Perform whole-genome sequencing of strains showing different RecF activities

  • Conduct comparative genomic analysis similar to that performed for B. licheniformis CBA7126

  • Identify sequence variations in recF and related genes

• Statistical analysis framework:

  • Use ANOVA or mixed models for multiple strain comparisons

  • Apply appropriate transformations for non-normally distributed data

  • Include strain as a random effect when appropriate

• Account for strain-specific factors:

  • Growth rates and physiological differences

  • Genetic background effects

  • Natural habitat and isolation source differences

• Visualization methods:

  • Create hierarchical clustering of strains based on RecF activity

  • Generate correlation plots of activity vs. genomic features

  • Develop principal component analysis to identify patterns across multiple variables

Genomic analysis of B. licheniformis strains has revealed that even closely related strains can exhibit differences, with similarities ranging from 99.80% to 99.99% . These seemingly small genomic differences may significantly impact RecF activity and function, requiring careful correlation between genomic features and phenotypic measurements.

What statistical methods are most appropriate for analyzing RecF-dependent DNA repair efficiency data?

The statistical analysis of RecF-dependent DNA repair efficiency requires careful consideration of data characteristics and experimental design:

When analyzing repair efficiency, researchers should:

• Begin with exploratory data analysis to assess data distribution characteristics

• Test for normality using Shapiro-Wilk or similar tests

• Apply appropriate transformations when necessary

• Use robust statistical methods when assumptions cannot be met

• Include appropriate controls to normalize for batch effects

For complex experimental designs involving multiple factors (e.g., strain, DNA damage type, RecF expression level), factorial ANOVA or mixed-effects models are particularly valuable. These approaches can identify main effects and interactions while accounting for random variation between experimental batches.

When comparing efficiency across conditions, similar to how RecT recombination efficiency was reported as "16.67%" under optimal conditions , ensure that appropriate confidence intervals or standard errors are included to represent the precision of the estimates.

How does the RecF-RecOR interaction in B. licheniformis compare with the RecET system in terms of recombination efficiency?

The comparison between RecF-RecOR and RecET recombination systems in B. licheniformis reveals important functional and efficiency differences:

The RecET system has demonstrated dramatically higher recombination efficiency compared to native systems in B. licheniformis, with studies showing a "105-fold enhancement in the recombination efficiency of the strain" . This exceptional efficiency makes the RecET system generally more suitable for genome engineering applications.

The RecF-RecOR system, while less efficient, is integrated with the cell's natural DNA repair pathways and operates in coordination with RecA. The two systems likely have different optimal applications:

• RecF-RecOR: Better suited for enhancing natural DNA repair processes and maintaining genomic stability

• RecET: Superior for genetic engineering applications requiring high-efficiency recombination

Understanding these differences helps researchers select the appropriate recombination system based on their specific experimental goals in B. licheniformis.

What genomic approaches can identify RecF regulatory elements in B. licheniformis strains?

Identifying RecF regulatory elements in B. licheniformis requires a multi-faceted genomic approach:

• Comparative promoter analysis:

  • Extract upstream regions of recF from multiple B. licheniformis strains

  • Employ motif discovery algorithms to identify conserved elements

  • Compare with known regulatory elements in related Bacillus species

• Transcriptome analysis:

  • Perform RNA-Seq under various growth and stress conditions

  • Identify transcription start sites using 5' RACE or similar techniques

  • Map transcriptional units containing recF

• ChIP-Seq for regulatory protein binding:

  • Identify transcription factors that bind near recF

  • Map binding sites at high resolution

  • Correlate binding with expression changes

• Analysis of genomic context:

  • Examine gene neighborhood conservation across strains

  • Identify potential operonic structures

  • Look for mobile genetic elements that might influence regulation

• Epigenomic profiling:

  • Map DNA methylation patterns near recF

  • Identify potential epigenetic regulatory mechanisms

These approaches should build upon existing genomic analysis of B. licheniformis strains like CBA7126, which has been thoroughly characterized with "PacBio RS II system" sequencing and analysis tools including "PacBio SMRT Analysis 2.3.0" . The genomic features of B. licheniformis strains can provide important context for understanding recF regulation:

Understanding these genomic features provides the foundation for targeted analysis of recF regulation in B. licheniformis.

How does the genomic neighborhood of recF vary across B. licheniformis strains, and what functional implications might this have?

The genomic neighborhood of recF can vary significantly across B. licheniformis strains, with important functional implications:

• Operon structure variations:

  • In some bacteria, recF is part of a conserved operon with DNA replication genes

  • Variations in operon structure affect co-regulation with other genes

  • Changes in gene order can disrupt regulatory elements

• Mobile genetic element insertions:

  • Prophages or transposable elements may insert near recF in some strains

  • These elements can introduce new regulatory sequences

  • Genomic analysis of B. licheniformis CBA7126 revealed strain-specific genomic features

• Regulatory element conservation:

  • Promoters, operators, and other regulatory sequences may vary

  • This can result in different expression patterns across strains

  • May explain strain-specific differences in DNA repair efficiency

• Synteny with related species:

  • Comparison with related Bacillus species can reveal evolutionary conservation

  • Highly conserved genomic neighborhoods suggest functional constraints

  • OrthoANI analysis of B. licheniformis CBA7126 showed varying degrees of similarity (99.80-99.99%) with other strains

• Impact on RecF function:

  • Genes adjacent to recF may influence its expression or activity

  • Proteins encoded by neighboring genes might interact with RecF

  • Co-evolution of functionally related genes in the neighborhood

What bioinformatics pipelines are most effective for analyzing RecF sequence conservation and functional domains across Bacillus species?

Effective bioinformatics pipelines for analyzing RecF across Bacillus species should include multiple complementary approaches:

• Sequence alignment and conservation analysis:

  • Multiple sequence alignment with MUSCLE or MAFFT

  • Conservation scoring using methods like Jensen-Shannon divergence

  • Visualization with tools like WebLogo for identifying conserved motifs

  • Integration with genome analysis tools used for B. licheniformis CBA7126

• Domain identification and functional annotation:

  • InterProScan for identifying conserved domains

  • HMMER for detecting remote homologs and domain architecture

  • CATH or SCOP classification for structural domain analysis

  • Correlation of domains with known functions

• Structural prediction and analysis:

  • AlphaFold2 or RoseTTAFold for structure prediction

  • PDB database mining for structural homologs

  • Molecular dynamics simulations to test functional hypotheses

  • Structure-based function prediction

• Evolutionary analysis:

  • Maximum likelihood phylogenetic tree construction

  • Selection pressure analysis using dN/dS ratios

  • Ancestral sequence reconstruction

  • Correlation with genome-wide phylogenetic patterns

• Integrated comparative genomics:

  • OrthoANI and ANI analysis as used for B. licheniformis CBA7126

  • Pangenome analysis to identify core and accessory genes

  • Synteny analysis using tools like Mauve or SynMap

  • Integration with metadata on strain phenotypes and habitats

These pipelines should be implemented with appropriate quality control measures and validation steps. The accuracy of functional predictions should be assessed by comparison with experimental data where available, and uncertainty should be clearly communicated when making functional inferences based solely on computational analysis.