The comprehensive analysis of genetic diversity within bacterial species is crucial for understanding their evolution, adaptation, and pathogenicity. Pangenome analysis, which examines the entire gene repertoire of a species rather than just a single reference genome, has emerged as a powerful tool in this endeavor. This study delves into two distinct approaches for pangenome construction and analysis: the traditional genome-based method and a refined subtype-balanced method utilizing multilocus sequence typing (MLST). By comparing these methodologies, we aim to highlight the advantages of incorporating subtype diversity for a more accurate and representative enumeration of genetic variation.
Fig 1: Comparative analysis of pangenome openness estimation methods across 12 microbial pathogens, highlighting the impact of MLST-based balancing on Heaps’ Law estimates.
Pangenome Openness: Genome-Based vs. Subtype-Balanced Estimates
Pangenome openness, a measure of the propensity for new genes to be introduced as more genomes are sequenced, serves as a key indicator of gene-level diversity within a species. Heaps’ Law, originally applied in linguistics, has been adapted to quantify pangenome openness by analyzing the relationship between pangenome size and the number of genomes. Traditionally, openness is estimated by fitting Heaps’ Law to the accumulating pangenome size as genomes are added in random order. However, this genome-based approach can be skewed when dealing with datasets that are not representative of the species’ full diversity.
Our analysis, leveraging 12,676 genomes across 12 bacterial species from the PATRIC database, revealed significant subtype bias in some species. For instance, a substantial proportion of E. faecium genomes belonged to a single MLST subtype. Such biases can lead to underestimation of true pangenome openness when using the genome-based method, as the analysis may primarily reflect the diversity within a dominant subtype rather than the entire species.
To address this limitation, we introduced a subtype-balanced approach. This method involves randomly selecting one genome per MLST subtype before applying Heaps’ Law, ensuring that the analysis accounts for the diversity across different subtypes. Comparing the two approaches, we found that MLST-based openness estimates were generally higher than genome-based estimates in 10 out of 12 species. Notably, the difference was most pronounced in species with strong subtype biases, such as E. faecium, where the MLST-based openness estimate nearly doubled. This highlights the importance of considering subtype diversity for a more accurate representation of species-wide pangenome openness.
Accuracy in Pangenome Size Extrapolation: MLST-Based Approach Outperforms Genome-Based Method
Beyond openness estimation, accurate prediction of pangenome size as more genomes are sequenced is crucial. To compare the predictive accuracy of the two methods, we performed Heaps’ Law fitting on the first half of genomes (or MLST subtypes) and evaluated the prediction error on the second half.
The MLST-based approach demonstrated superior performance in pangenome size extrapolation. The median mean absolute error (MAE) was lower in 11 out of 12 species within the fitted region and in 9 out of 12 species in the extrapolated region, despite using fewer data points for fitting compared to the genome-based method. This indicates that the subtype-balanced approach not only provides a more accurate estimate of current openness but also offers improved predictive power for future pangenome expansion.
While the MLST-based approach generally outperformed the genome-based method, there were exceptions. In P. aeruginosa and S. enterica, the genome-based approach exhibited lower MAE. This could be attributed to these species having poorer overall fits to Heaps’ Law, suggesting that the model’s accuracy can vary depending on the species’ genetic architecture. However, overall, the MLST-based Heaps’ Law approach proves to be a more robust and consistent method for extrapolating pangenome size, especially for datasets with subtype biases.
Frequency-Based Pangenome Division: Unveiling Core, Accessory, and Unique Genomes
Moving beyond the overall pangenome characteristics, we investigated the distribution of gene frequencies to understand the composition of pangenomes in more detail. Gene frequency, defined as the number of genomes in which a gene is observed, provides insights into the prevalence and conservation of genes within a species.
Analyzing gene frequency distributions across all 12 species revealed a consistent pattern: a peak for rare genes and another for highly conserved genes. This bimodal distribution suggests a natural division of the pangenome into three categories:
- Core Genome: Highly conserved genes present in the majority of genomes, representing essential functions.
- Accessory Genome: Genes with intermediate frequencies, contributing to species diversity and adaptation.
- Unique Genome: Rare genes found in only a few genomes, often poorly characterized and potentially strain-specific.
Fig 2: Power function based frequency division of the Campylobacter coli pangenome into core, accessory and unique genomes, illustrating the gene frequency distribution and cumulative distribution.
To quantitatively define these categories, we developed a novel approach based on fitting the gene frequency distribution to the sum of two power functions. This method accurately captured the distribution’s shape and allowed for the determination of frequency thresholds that delineate the core, accessory, and unique genomes. The resulting core genomes ranged from 4.3% to 34.2% of the total pangenome size, while accessory genomes ranged from 5.0% to 38.1%. This frequency-based division provides a valuable framework for focusing subsequent analyses on specific gene sets, such as the conserved core or the diverse accessory genome, rather than the entire pangenome.
Functional Enrichment in Core and Accessory Genomes: Metabolic and Defense Specialization
To gain functional insights into the core and accessory genomes, we performed functional enrichment analysis using Clusters of Orthologous Groups (COG) categories. This analysis revealed distinct functional profiles for these pangenome components.
Core genomes consistently showed enrichment in COGs related to essential cellular functions, including:
- Energy production and conversion
- Amino acid, nucleotide, and coenzyme transport and metabolism
- Translation, ribosomal structure, and biogenesis
- Post-translational modification, protein turnover, and chaperones
These enrichments highlight the core genome’s role in maintaining fundamental metabolic and cellular processes necessary for survival across all strains of a species.
Accessory genomes, while exhibiting weaker enrichments, showed significant associations with:
- Intracellular trafficking, secretion, and vesicular transport
- Defense mechanisms
These functional categories suggest that accessory genes contribute to niche adaptation, interaction with the environment, and defense against external threats, driving the diversity observed within species. Unique genomes, in contrast, showed limited functional enrichment, likely due to their rarity and under-characterization.
Fig 3: Functional enrichment comparison between core and accessory genomes across 12 species, displaying the number of genes and COG functional category enrichments.
Further analysis of Gene Ontology (GO) terms reinforced these findings, with core genomes enriched in ribosome and RNA processing terms, and accessory/unique genomes showing less specific enrichments. This functional dissection of the pangenome underscores the specialization of core genes for essential housekeeping functions and accessory genes for adaptive and niche-specific roles.
Sequence-Level Conservation and Variation in Core Genes: Domain-Dependent Diversity
To investigate genetic diversity at the sequence level, we analyzed the frequency of protein sequence variants within core genes. We calculated allelic entropy, a measure of sequence diversity, for coding sequences and flanking intergenic regions (5’IG and 3’IG).
Interestingly, we observed limited correlation between coding sequence variability and intergenic sequence variability, indicating independent evolutionary pressures on these regions. Functional enrichment analysis of genes with high or low sequence diversity revealed a consistent enrichment of translation, ribosomal structure, and biogenesis (COG J) among the least sequence-diverse core genes. This suggests strong selective pressure to conserve the sequences of genes involved in fundamental translational processes.
Fig 5: Functional enrichment analysis of core genes based on sequence diversity in coding and intergenic regions, highlighting genes with diverse or conserved sequences and their COG enrichments.
Conversely, genes with high sequence diversity did not show strong functional enrichments, suggesting that sequence variation in other core genes may be driven by diverse factors beyond specific functional categories. Examining MLST genes, we found a range of sequence diversity, indicating that even genes used for typing can exhibit varying degrees of sequence polymorphism.
Domain-Level Mutation Enrichment in Conserved Core Genes: Aminoacyl-tRNA Synthetase Hotspots
At the highest resolution, we investigated the positional distribution of sequence diversity within conserved core genes. By analyzing multiple sequence alignments and domain annotations, we identified protein domains that were either enriched or depleted for mutations across species.
Our analysis revealed that mutation enrichment and depletion were domain-dependent. A significant number of domains, both enriched and depleted, were identified, spanning a diverse range of functions. Notably, domains related to aminoacyl-tRNA synthetases (AARSs) were overrepresented among mutation-enriched domains.
Fig 6: Domain-level mutation enrichment analysis across 76 core genes present in all 12 species, showcasing domain entropy percentiles and mutation enrichment patterns in specific domains.
Further examination of AARS domains revealed a functional association with mutation enrichment. Domains involved in editing, anticodon binding, or tRNA binding within AARSs tended to be mutation-enriched or mutation-neutral, while catalytic domains were predominantly mutation-depleted. This suggests that specific functional regions within even highly conserved proteins can experience different evolutionary pressures, with some domains tolerating or even requiring sequence variation while others remain highly constrained.
Fig 7: Mutation enrichment in Aminoacyl-tRNA Synthetase domains, demonstrating the functional specificity of mutation enrichment within AARS domains across 12 species.
Conclusion
In conclusion, this comparative analysis highlights the importance of considering subtype diversity in pangenome analysis. The MLST-based approach provides a more accurate estimation of pangenome openness and improved prediction of pangenome size compared to the traditional genome-based method, particularly in datasets with subtype biases. Furthermore, our study provides a comprehensive characterization of pangenome structure, functional specialization of core and accessory genomes, and domain-level sequence diversity within conserved genes. These findings contribute to a deeper understanding of bacterial genetic diversity and provide valuable methodological insights for future pangenome studies.
