Recombinant Bacillus licheniformis DNA-directed RNA polymerase subunit beta (rpoB), partial

What is the rpoB gene in Bacillus licheniformis and why is it important for molecular studies?

The rpoB gene encodes the beta subunit of DNA-directed RNA polymerase, a crucial enzyme for transcription in bacteria. In B. licheniformis, rpoB spans approximately 3,800 bp and encodes a protein essential for cellular function. The importance of rpoB stems from its status as a housekeeping gene with both conserved and variable regions, making it particularly valuable as a phylogenetic marker.

Unlike 16S rRNA genes, which have limited resolution for closely related species, rpoB sequences provide greater discriminatory power for species-level identification within the Bacillus genus . The gene's moderate evolutionary rate makes it ideal for distinguishing between closely related bacterial taxa, such as B. licheniformis and B. paralicheniformis, which are often misidentified due to their significant sequence similarity in other genetic markers .

What are the standard protocols for amplifying and sequencing the rpoB gene from B. licheniformis?

For amplification of the rpoB gene from B. licheniformis, researchers typically use PCR with the following protocol:

• Primer selection: Use primers targeting a ~580 bp fragment of the rpoB gene. Based on published research, the following primers have proven effective:

  • rpoB-f: 5'-AGG TCA ACT AGT TCA GTA TGG ACG-3'

  • rpoB-r: 5'-AAG AAC CGT AAC CGG CAA CTT-3'

• PCR amplification conditions:

  • Initial denaturation: 5 min at 95°C

  • 35 cycles of: 10 s at 95°C, 10 s at 56°C, 30 s at 72°C

  • Final extension: 7 min at 72°C

For sequencing, purified PCR products should be sequenced using the dideoxy chain termination method. The resulting sequences should be aligned using software such as the Staden Package or CLUSTALW, then trimmed to be in frame .

A study by Genotyping of B. licheniformis found that a 318 bp region of the rpoB gene provided sufficient variation for reliable identification and phylogenetic analysis .

How does the rpoB gene sequence vary among different strains of B. licheniformis?

Studies have shown that the rpoB gene sequences can distinguish between two main lineages within B. licheniformis, designated as groups "A" and "B" . These two distinct subgroups are consistently observed in phylogenetic analyses.

Sequence analysis of multiple B. licheniformis strains has revealed:

• Group B contains the majority of strains (approximately 74%), including the type strain ATCC14580

• Strains in group B tend to be more closely related to each other than those in group A

• The genetic relationship between these groups is conserved across multiple loci, including rpoB

Interestingly, no relationship between the source of isolates and their clustering pattern has been observed, indicating that the genetic division is not correlated with ecological niche or isolation source .

Some strains, such as the food contaminant NVH1032, show unique rpoB sequences that don't cluster with either of the two main groups, suggesting potential evolutionary divergence or adaptation to specific environments .

How effective is rpoB sequence analysis in distinguishing B. licheniformis from closely related species?

The rpoB gene has proven highly effective for distinguishing between closely related Bacillus species. Studies have demonstrated that:

• rpoB sequences can clearly differentiate B. licheniformis from B. paralicheniformis, which are often misidentified due to their high similarity using other genetic markers

• Within the "B. cereus group," rpoB sequence analysis separated B. anthracis into a distinct clade, while B. cereus and B. thuringiensis could not be differentiated, suggesting varying levels of discriminatory power depending on the species group

• In a comprehensive study, determined rpoB sequences (318 bp) of multiple B. anthracis strains were identical, providing a stable marker for this species

The effectiveness of rpoB-based identification has been confirmed through multiple approaches:

For maximum reliability, rpoB should be used as part of a Multi-Locus Sequence Typing (MLST) scheme rather than as a single marker, particularly when identifying closely related strains within the same species .

How is the rpoB gene incorporated into MLST schemes for B. licheniformis?

The rpoB gene serves as a core component in MLST schemes for B. licheniformis. Research has established an effective MLST approach using six housekeeping genes, including:

• adk (adenylate kinase)

• ccpA (transcriptional regulator)

• recF (recombination protein F)

• rpoB (RNA polymerase beta subunit)

• spo0A (response regulator)

• sucC (succinyl-CoA synthetase, beta subunit)

This combination of genes was selected after evaluating nine candidate housekeeping genes, and was determined to provide the highest level of discrimination while maintaining congruence in phylogenetic trees .

The MLST methodology involves:

• Amplifying the target regions of all six genes using specific primers

• Sequencing the amplicons and aligning the sequences

• Assigning unique allele numbers to each distinct sequence for each locus

• Defining sequence types (STs) based on the combination of alleles across all six loci

• Performing phylogenetic analysis using concatenated sequences or allelic profiles

Using this MLST scheme, researchers identified 27 different sequence types among 53 B. licheniformis strains, demonstrating its high discriminatory power .

What statistical methods are most appropriate for analyzing rpoB sequence data in evolutionary studies?

For evolutionary studies of rpoB data, several statistical approaches have proven effective:

• Phylogenetic tree construction methods:

  • Neighbor-Joining (NJ) method with branch lengths estimated by the Maximum Composite Likelihood method has been successfully used for rpoB phylogeny

  • Bootstrap testing (typically with 500 replicates) should be applied to assess branch quality

• Recombination analysis:

  • The Index of Association (IA) calculation can test for linkage equilibrium between alleles

  • This should be performed on both the complete dataset and a reduced dataset containing only one isolate per sequence type to avoid bias toward clonality

• Sequence variation analysis:

  • The ratio of nonsynonymous to synonymous substitutions (dN/dS) provides insights into selective pressure on the gene

  • Analyzing nucleotide differences between sequences helps quantify genetic diversity

Statistical software commonly used for these analyses includes:

• START2 for calculating nucleotide differences and dN/dS ratios

• MEGA software for constructing and visualizing phylogenetic trees

• BioNumerics for generating allelic profiles and performing cluster analysis using categorical coefficients

For optimal results, concatenated sequences of all MLST loci should be used for phylogenetic analysis rather than rpoB alone, as this provides a more robust evolutionary framework .

What controls should be included when using rpoB for species identification?

To ensure reliable results when using rpoB for species identification, researchers should include the following controls:

• Positive controls:

  • Reference strains of target species (e.g., B. licheniformis ATCC14580)

  • Closely related species with confirmed identity (e.g., B. paralicheniformis KACC 18426)

  • Strains with known sequence types if performing MLST analysis

• Negative controls:

  • Non-target Bacillus species (e.g., B. subtilis, B. megaterium)

  • PCR negative control (no template) to detect contamination

  • Extraction blank to identify DNA extraction contamination

• Internal controls:

  • Amplification of a universal bacterial marker (e.g., 16S rRNA) to confirm DNA quality

  • Known concentration standards if performing quantitative analysis

  • Multiple technical replicates to ensure reproducibility

• Analytical controls:

  • Sequence both strands to verify sequence accuracy

  • Include reference sequences in phylogenetic analyses

  • Use multiple phylogenetic methods to confirm tree topology

In a study distinguishing B. anthracis using rpoB, researchers included 10 strains of B. anthracis, 16 of B. cereus, 10 of B. thuringiensis, 1 of B. mycoides, and 1 of B. megaterium as controls to validate the specificity of their approach .

How can researchers resolve contradictory results between rpoB and other genetic markers?

When faced with contradictory results between rpoB and other genetic markers, researchers should implement the following methodological approach:

• Comprehensive assessment:

  • Analyze the sequence quality and coverage for all markers

  • Check for sequencing errors, chimeric sequences, or contamination

  • Evaluate the discriminatory power of each marker for the specific taxonomic level in question

• Recombination analysis:

  • Test for evidence of horizontal gene transfer or recombination events

  • Calculate the Index of Association (IA) to detect linkage disequilibrium

  • Apply specialized software like RDP4 to identify potential recombination breakpoints

• Expanded marker approach:

  • Increase the number of genetic loci analyzed (MLST approach)

  • Include markers with different evolutionary rates

  • Consider whole genome sequencing for definitive resolution

• Phenotypic correlation:

  • Compare genetic results with phenotypic characteristics

  • Test for biochemical or physiological traits that differentiate the species in question

  • Evaluate ecological or isolation source data for additional context

Research has shown that for B. licheniformis and related species, combining rpoB with other markers provides more reliable results than single-gene analysis. For example:

• The combination of six housekeeping genes (adk, ccpA, recF, rpoB, spo0A, and sucC) in MLST provides robust phylogenetic resolution

• Species-specific markers based on secondary metabolite genes (like fenC and fenD for B. paralicheniformis) can complement rpoB-based identification

• In cases of potential horizontal gene transfer, combining phylogenetic markers with markers for species-specific bacteriocins (like paralichenicidin or lichenicidin) can help resolve contradictions

How should researchers interpret synonymous versus non-synonymous substitutions in rpoB sequences?

The analysis of synonymous (dS) and non-synonymous (dN) substitutions in rpoB sequences provides valuable insights into evolutionary pressures and functional constraints:

• Interpretation framework:

  • dN/dS ratio < 1: Indicates purifying (negative) selection, suggesting functional constraints

  • dN/dS ratio ≈ 1: Suggests neutral evolution

  • dN/dS ratio > 1: Indicates positive (diversifying) selection, possibly adaptive evolution

• Methodological approach:

  • Calculate dN/dS ratios using software like START2

  • Perform sliding window analysis to identify domains under different selective pressures

  • Compare dN/dS ratios across different lineages or species groups

• Functional implications:

  • Low dN/dS in catalytic domains suggests functional conservation

  • Higher dN/dS in surface-exposed regions may indicate immune selection or environmental adaptation

  • Variable dN/dS across the gene may reveal mosaic evolution patterns

Studies on B. licheniformis rpoB have shown that this gene generally exhibits purifying selection (dN/dS < 1), consistent with its essential housekeeping function . This pattern of conservation makes rpoB suitable for phylogenetic analysis while still providing sufficient variation for species identification.

The interpretation of synonymous/non-synonymous substitutions should consider:

• The specific region of the rpoB gene being analyzed

• The taxonomic level of comparison (within species vs. between species)

• Comparison with other housekeeping genes in the same strains

• Potential recombination events that may affect localized selection patterns

What threshold values should be used when comparing rpoB sequences for species delineation?

Establishing appropriate threshold values for species delineation using rpoB sequences requires careful consideration of empirical data and methodological consistency:

• Sequence similarity thresholds:

  • For Bacillus species delineation, rpoB sequence similarity of 97-98% is generally considered the threshold between species

  • Within B. licheniformis, strains typically show >99% similarity in rpoB sequences

  • Between distinct lineages (groups A and B) of B. licheniformis, rpoB similarity remains high but consistent clustering patterns are observed

• Complementary approaches:

  • Average Nucleotide Identity (ANI) of >95-96% generally indicates strains belong to the same species

  • In MLST analysis, allelic profiles rather than simple sequence similarity should be used to define sequence types

  • Concatenated sequences of multiple genes provide more robust threshold values than single genes

• Empirical calibration:

  • Thresholds should be calibrated using well-characterized reference strains

  • Correlation with DNA-DNA hybridization values (historically the gold standard)

  • Validation against whole genome sequence data where available

The application of these thresholds has been demonstrated in research:

Researchers should note that threshold values should not be applied rigidly but interpreted in the context of other genetic and phenotypic data to avoid misclassification of borderline cases .

How can researchers integrate rpoB data with whole genome sequencing analyses?

Integrating rpoB sequence data with whole genome sequencing (WGS) analyses enhances the robustness of bacterial identification and evolutionary studies through the following methodological approaches:

• Multi-scale comparative analysis:

  • Use rpoB as an initial screening tool for strain identification

  • Validate rpoB-based classification with whole genome metrics like ANI

  • Compare rpoB phylogeny with whole-genome SNP-based phylogenies to identify discrepancies

• Technical integration:

  • Extract rpoB sequences from whole genome data using bioinformatic tools

  • Ensure consistent sequence regions when comparing extracted sequences with PCR-amplified sequences

  • Standardize annotation and gene boundary definitions across datasets

• Evolutionary context analysis:

  • Examine the genomic context of rpoB to identify potential recombination events

  • Analyze synteny and gene order conservation around the rpoB locus

  • Investigate selection pressures on rpoB in the context of genome-wide selection patterns

• Practical workflow:

  • Start with rpoB-based screening of isolates

  • Select representative strains for whole genome sequencing

  • Use WGS data to validate and refine rpoB-based classifications

  • Develop and test new markers based on WGS comparative analysis

Research has demonstrated the effectiveness of this integrated approach:

• Studies comparing B. licheniformis strains found concordance between rpoB-based phylogeny and whole-genome orthoANI values (>99% similarity for same-species strains)

• Genomic analysis has confirmed the two main lineages of B. licheniformis originally identified through MLST including rpoB

• WGS analysis has enabled the identification of additional genetic markers (such as fenC and fenD) that complement rpoB-based identification

By integrating rpoB analysis with WGS data, researchers can achieve more accurate taxonomic assignments, better understand evolutionary relationships, and develop more targeted identification methods for specific research questions .

How has rpoB analysis contributed to understanding B. licheniformis applications in biotechnology?

The rpoB-based phylogenetic analysis of B. licheniformis has significantly advanced our understanding of this species' biotechnological applications through several mechanisms:

• Strain identification and characterization:

  • Accurate identification of B. licheniformis strains using rpoB has enabled researchers to link specific genetic backgrounds to desirable industrial traits

  • Distinction between B. licheniformis and B. paralicheniformis has revealed differences in stress tolerance and metabolic capabilities relevant to industrial applications

• Genetic engineering optimization:

  • Understanding of rpoB sequences has facilitated the development of expression systems in B. licheniformis

  • Knowledge of genetic relationships has informed strain selection for metabolic engineering projects

• Quality control in industrial processes:

  • rpoB-based identification methods help ensure strain purity in industrial fermentations

  • Monitoring for genetic stability during long-term industrial cultivation

• Correlation of genetic lineages with industrial properties:

  • Studies have found that strains from different rpoB-defined lineages may exhibit different enzyme production capabilities

  • This information guides selection of optimal strains for specific applications

Research examples demonstrating these contributions include:

• A study on optimized expression of alkaline protease in B. licheniformis used rpoB as part of the genetic characterization of industrial strains, helping to identify specific genetic backgrounds associated with high production levels

• Phylogenetic analysis based on rpoB and other genes revealed distinct lineages of B. licheniformis with potentially different industrial applications

• The clear distinction between B. licheniformis and the closely related B. paralicheniformis using rpoB-based methods has helped researchers select appropriate strains for specific biotechnological applications

What emerging methods are improving rpoB-based identification of Bacillus species?

Several emerging methods are enhancing the power and efficiency of rpoB-based identification of Bacillus species:

• High-throughput sequencing approaches:

  • Amplicon-based metagenomic sequencing targeting rpoB

  • Shotgun metagenomics with bioinformatic extraction of rpoB sequences

  • Long-read sequencing technologies that capture the complete rpoB gene

• Advanced bioinformatic tools:

  • Machine learning algorithms for species prediction based on rpoB sequences

  • k-mer based approaches for rapid identification without full alignment

  • SNP-based identification methods that focus on key diagnostic positions

• Multiplex and integrated approaches:

  • Multiplex PCR systems targeting rpoB and complementary genes simultaneously

  • Integration of rpoB with species-specific markers like secondary metabolite genes

  • Combination of rpoB with MALDI-TOF mass spectrometry profiles

• Portable sequencing technologies:

  • Field-deployable sequencing platforms for rapid on-site identification

  • Real-time PCR coupled with high-resolution melt curve analysis for rpoB variants

  • Isothermal amplification methods for resource-limited settings

Research has demonstrated these advances:

• Development of multiplex PCR systems that generate B. anthracis-specific amplicons based on rpoB sequences combined with virulence plasmid detection

• Creation of complementary genetic markers (fenC and fenD) that work alongside rpoB for definitive identification of B. paralicheniformis versus B. licheniformis

• Implementation of k-mer based approaches as alternatives to full sequence alignment for rapid species identification

These emerging methods are particularly valuable for:

• Environmental monitoring and food safety applications requiring rapid results

• Clinical diagnostics where accurate species identification affects treatment decisions

• Industrial settings where contamination monitoring is essential

• Research projects involving complex environmental samples with multiple Bacillus species

What are the future research priorities for rpoB analysis in Bacillus licheniformis?

Future research priorities for rpoB analysis in B. licheniformis should address current knowledge gaps and leverage technological advances:

• Expanding phylogenomic integration:

  • Comprehensive comparison of rpoB-based phylogeny with whole-genome phylogenies across diverse strains

  • Investigation of recombination patterns and horizontal gene transfer events affecting rpoB

  • Development of integrated evolutionary models that incorporate rpoB and genome-wide data

• Functional studies of rpoB variants:

  • Analysis of how rpoB sequence variations affect transcriptional efficiency and fidelity

  • Investigation of potential links between rpoB variants and stress responses or antibiotic resistance

  • Study of how rpoB mutations might influence industrial performance characteristics

• Methodological refinements:

  • Standardization of rpoB amplification and sequencing protocols for improved inter-laboratory reproducibility

  • Development of curated reference databases specifically for Bacillus rpoB sequences

  • Creation of automated analysis pipelines optimized for Bacillus species identification

• Application expansions:

  • Use of rpoB analysis to track B. licheniformis strains in environmental and industrial settings

  • Integration of rpoB-based identification with functional genomics in biotechnology applications

  • Development of rpoB-targeted approaches for specific detection of beneficial or problematic strains

Promising research directions include:

• Investigating the correlation between rpoB lineages and probiotic properties, as studies have shown B. licheniformis has protective effects on growth performance and immunity in animal models

• Exploring how rpoB variants might influence expression system efficiency, building on work showing B. licheniformis as a superior expression platform

• Developing targeted approaches to rapidly distinguish food-contaminating strains using rpoB and complementary markers, addressing food safety concerns

• Examining rpoB evolution in different ecological niches to understand adaptation mechanisms and predict industrial performance characteristics