Introduction

Borrelia burgdorferi sensu stricto is the genospecies of the B. burgdorferi sensu lato complex with a wide geographic distribution in the northern hemisphere, including North America, and Europe1. The enzootic cycle typically involves small mammals and birds as competent reservoir hosts2. Humans are incidental hosts and do not contribute to onward transmission3,4. The geographic range of I. scapularis and B. burgdorferi s.s. has expanded northward in recent decades, especially into eastern and central Canada, raising public health concerns. Borrelia burgdorferi s.s. is the main bacterium that causes Lyme disease in North America, where it has a complex clonal structure as revealed using molecular methods such as multilocus sequence typing (MLST)5,6 and 16–23 S ribosomal RNA intergenic spacer (rrs-rrlA: IGS)7. Plasmid markers include the outer surface protein A (ospA) gene located on the lp54 plasmid8 and the outer surface protein C (ospC) gene of the cp26 plasmid9. These methods have uncovered a high level of diversity. Multilocus sequence typing (MLST) has resolved 180 sequence types (STs) in the USA and Canada to date5 (https://pubmlst.org/), while sequencing of the plasmid-located ospC gene has identified 47 types and subtypes of B. burgdorferi s.s. in North America9,10,11,12. Whilst these methods are not always consistent5,13,14, these data have shed light on key phenotypes such as disease severity in humans, reservoir host associations, and local adaptations. ospC alleles A, B, I, and K and RST1 were associated with disseminated Lyme disease, while ospC alleles J, T, U and RST3 are associated with more localized clinical symptoms of Lyme disease15,16,17. RST1, ST1, and ospC allele A have been found to be associated with white-footed mice, and RST2, and ospC G with eastern chipmunks, suggesting possible onset of specialization for these host species and evidence of radiative adaptation of B. burgdorferi s.s. in North America18. Variation between geographic regions has also been identified e.g., in the USA, ST1 is found exclusively in the Northeast, ST2 and ST5 in California, and ST55 and ospC allele A in the Upper Midwest5,19.

In North America, Lyme disease caused by B. burgdorferi s.s. has the highest incidence in the Northeastern and upper Midwestern regions of the United States. However, the phylogeographic patterns of this species reflect complex forces over millenia, and remain dynamic in the present day20. It is thought that anthropogenic changes to land use from woodland to agricultural land throughout the post-Columbian period, forced human-biting northern clades of Ixodes scapularis ticks21,22 and their key reproduction hosts (white-tailed deer), bird host, and key rodent reservoir hosts for B. burgdorferi s.s., into limited refugia in the upper Midwest and Northeast of the US producing a bottleneck for B. burgdorferi s.s. populations23. However, during the 20th century, industrialisation resulted in migration of human populations from agricultural communities to urban centres, and over time agricultural land has reverted to woodland and deer, rodent, tick and B. burgdorferi s.s. populations began to expand. By the late 1970’s Lyme disease was a significant public health issue in the northern US24. As well as land use change in the US, climate warming has rendered parts of Southern Canada climatically suitable for I. scapularis populations25,26,27.

This dynamic nature of B. burgdorferi s.s. populations over the last century has resulted in complex and varied population structures. Margos et al. (2012) identified that in the USA, B. burgdorferi s.s. populations are geographically structured into three sub-populations (northeastern, midwestern and western), with different clonal lineages being found in different regions. MLST data point to relatively recent introductions into Canada from multiple refugial southern populations in the USA28,29. In addition to geographic changes, the expansion of B. burgdorferi s.s. populations has been expected to undergo adaptive radiation, accompanied by multi-niche polymorphism30. Borrelia burgdorferi s.s. is a generalist capable of using a wide range of vertebrate species as reservoir host, but there is evidence of emerging host-genotype associations in North America18. Possible public health consequences of broadening diversity of B. burgdorferi s.s. include strain-specific manifestations of Lyme disease in affected people or impacts on sensitivity of serological diagnostic tests31.

The question is whether or not different genotyping methods can distinguish between these important phenotypes32. This is more likely the case with markers associated with the surface of the bacterium and which are carried on plasmids, such as OspC than with non-coding IGS sequences or other chromosomal markers. Here we explore the degree of consistency between plasmid and chromosomal markers.

An important related question concerns the extent of clonality within B. burgdorferi s.s. populations and how this influences their genetic structure and evolutionary dynamics. In the B. burgdorferi s.l. species complex, inter-specific recombination events are relatively uncommon33 compared to, for example, Enterobacteriaceae, possibly due to slow growth rates in hostile host and vector environments33,34. Nevertheless, recombination does occur, even on the chromosome35, although most recombination events are between closely related strains that are more likely to be adapted to the same host31.

In this study, we investigate these questions by analyzing the genome sequences of 64 B. burgdorferi s.s. strains collected in a previous study by Tyler and colleagues (2018) from three regions in Canada (Manitoba, Ontario, and Nova Scotia), representing a broad spectrum of genetic diversity and ecological contexts. Therefore, this is primarily a population genomics study investigating the phylogeography and population structure of B. burgdorferi in Canada, informed by genomic markers from both core and accessory genomes, with consideration of underlying evolutionary processes (recombination vs. mutation).

Materials and methods

Samples used and genome Preparation

Sixty-four Borrelia burgdorferi s.s. strains were isolated from host-seeking Ixodes scapularis ticks and sequenced by Tyler et al. (2018). Ticks were collected in 2016 by drag sampling in 10 locations in three Lyme-endemic regions of Canada: Buffalo Point and Roseau River in Manitoba; Big Grassy, Big Island, Birch Island, Manitou Rapids in northwestern Ontario; and Bedford, Lunenburg, Pictou, Shelburne in Nova Scotia. DNA libraries were prepared using TruSeq sample preparation kits (Illumina, San Diego, CA) and sequenced with 300 bp paired-end reads on the Illumina MiSeq platform. Genome assembly was performed using SPAdes v3.9 with contigs ≥ 1,000 bp. The complete genome sequences have been deposited in GenBank (BioProject accession number PRJNA416494).

Gene panel selection

From the assembled genomes, we extracted genes representing both plasmid-encoded and chromosomal diversity:

Plasmid-encoded genes: Eleven surface-exposed protein-encoding genes important for Lyme disease diagnostics and pathogenicity: the C6 peptide (IR6 region of the VlsE1 gene, lp28-1 plasmid in B31), dbpA, dbpB, fibronectin-binding protein P35, oms28, ospA, ospB, ospC, ospD, P37, and P45-13.

Chromosomal genes

Four antigens (bmpA, flaB, oms66, P83-100) and eight housekeeping genes from the MLST scheme (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA).

Ribosomal markers (rrs-rrlA non-coding region): Variation in the 16–23 S intergenic spacer was characterized at three levels: (i) IGS typing (the full region), (ii) RSP alleles (ribosomal spacer patterns), and (iii) RST groups (three categories based on RFLP patterns).

Comparisons were also made with 24 reference B. burgdorferi s.s. genomes available in GenBank (Table S2).

Phylogenetic analysis

To test the validity of concatenating multiple genes, we used 13 chromosomal loci, including four chromosomal antigens (bmpA, flaB, oms66, P83-100), eight housekeeping genes (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA), and the 16–23 S intergenic spacer marker. we applied topology tests (Approximately Unbiased [AU], Shimodaira–Hasegawa [SH], Kishino–Hasegawa [KH]) and Robinson-Foulds (RF) distance comparisons using IQ-TREE v2.1.236. Both a partitioned model (gene-specific evolutionary rates) and a concatenated model (single evolutionary rate) were evaluated.

These tests ensured that concatenation did not introduce significant bias and that the phylogenetic approach accurately captured evolutionary relationships. Maximum likelihood (ML) phylogenetic trees were then constructed using MEGA version 537, with alignments performed in MAFFT tool (v7.450)38,39 provided in Geneious prime version 23.1.1. Concatenated core genome sequences (chromosomal antigens, housekeeping genes, 16–23 S marker) and individual accessory genome sequences were analyzed.

Congruence between core and accessory genome trees was assessed using a binary scoring system for monophyly and haplotype correspondence, summarized for each plasmid gene and classification scheme (MLST, ospC MGs, IGS, RSP, RST), as opposed to using more conventional metrics such as Robinson-Foulds (RF) distance40,41,42. This decision was driven by the fact that accessory genome trees often do not contain the same set of taxa as the core genome tree (i.e., certain taxa lacked the presence of some plasmid genes). To assess the congruence between the core genome tree and accessory genome trees, we evaluate the monophyly and haplotype correspondence of groups in the core genome tree with the same groups in the accessory genome trees. Specifically, for each monophyletic group defined in the core genome tree, we visually inspected whether that group remained monophyletic in the accessory genome tree (i.e., all members of the group cluster together); and corresponded to the same haplotype (i.e., the group retains its genetic identity) in the accessory genome tree.

For each accessory gene, a binary score was assigned for each group. A score of 1 was given if the group remained monophyletic and corresponded to the same haplotype in the accessory genome tree. A score of 0 was assigned if the group was not monophyletic or did not correspond to the same haplotype.

These scores were summarized in a table for each classification method (MLST, ospC major groups, IGS, RSP, RST), and the congruence score for each plasmid gene was calculated as the percentage of congruent groups (i.e., number of groups scored as 1) relative to the total number of groups. This allowed us to assess how well the phylogenetic relationships derived from accessory genome trees aligned with those from the core genome.

Multi-locus sequence typing (MLST) and geographic distribution analysis

To validate geographic distribution, MLST data were retrieved from PubMLST.org (accessed December 2024). Only entries with complete metadata (host, tick species, geographic location) were included. MLST profiles were cross-referenced with publicly available WGS data. Reported geographic distributions were mapped to study regions, and concordance with phylogenetic clusters was qualitatively assessed.

Recombination versus mutation analysis

To further investigate the evolutionary dynamics of core and accessory genomes, ClonalFrameML version 343 was used to estimate recombination (R) versus mutation (θ) rates for the core genome and each gene. Higher R/θ values indicate that recombination played a more prominent role in the evolution of these genes compared to mutation-driven divergence.

Statistical analysis

To explore the relationships between core and accessory genomes and assess population structure, we conducted a series of statistical analyses encompassing correlation testing, clonality measures, and network-based modularity, including:

Correlation analysis

Spearman correlation coefficients were calculated between plasmid and core genes based on similarity to the B31 reference genome, to evaluate whether these markers share similar evolutionary trajectories. Positive correlations between core and accessory genes suggest that the genes have followed similar evolutionary trajectories with respect to selection and recombination44,45.

Hierarchical clustering

Performed using DATAtab online tool46 to assess clade divergence under recombination/mutation, providing insights into how genomic processes shape phylogenetic stability.

Clonality assessment

goeBURST analysis in Phyloviz v247 identified clonal complexes via single-locus variants (SLVs). Clonality ratios (complexes vs. singletons) were compared between three regions (Manitoba, Ontario, and Nova Scotia), with a Mann–Whitney U test48,49 and Cliff’s Delta50 applied to quantify differences and effect sizes, thereby testing for geographic variation in clonality. These analyses were conducted in Python using the scipy.stats51 and effectsize52 libraries.

Network analysis

Conducted in Gephi v0.10.153 within three geographic regions (ON, MB, and NS) to detect modularity (Q index) in genetic clustering, allowing assessment of how strains form cohesive subpopulations based on sequence similarity to reference genomes (Table S2). Genetic similarity was assessed using BLASTn, and only high-confidence alignments with ≥ 99% sequence identity to reference genomes were retained to ensure meaningful homology and minimize spurious connections.

Results

Genetic diversity and representativeness of the dataset

Our dataset of 64 whole-genome sequences captures a broad spectrum of B. burgdorferi s.s. genetic diversity within Canada. It includes 33 sequence types (STs), representing over one-third of the 90 known STs in the country, covering major genomic lineages circulating in different regions. Additionally, the dataset incorporates 22 ospC major groups and subgroups (out of 32 described in Canada), along with 10 intergenic spacer (IGS) types, 12 ribosomal spacer patterns (RSPs), and 3 restriction site types (RSTs). All samples were collected in 2016, minimizing potential temporal bias. While our aim was to capture the overall genetic diversity present in Canada, the dataset was not designed to exhaustively represent the full diversity within each individual region.

Comparative phylogenetic analysis of partitioned and concatenated evolutionary models

To evaluate the impact of different evolutionary models on phylogenetic inference, we compared partitioned and concatenated models of all chromosomal markers (BmpA, FlaB, oms66, P83-100, clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA and rrs-rrlA) using IQ-TREE (Fig. S1 and S2). The phylogenetic inference using partitioned and concatenated evolutionary models revealed identical tree topologies, with only minor differences in node support values (Fig. S1, S2). Notably, the only topological difference was observed when comparing the concatenated GTR model (Fig. S2) to the Jukes-Cantor (JC) model (Fig. 1): in the JC tree, Groups 1 and 2 formed a single monophyletic cluster, whereas in the GTR tree, they appeared as two distinct, well-supported clades. This difference arises because the JC model assumes equal substitution rates among all nucleotide changes, making it less sensitive to subtle differences between lineages. As a result, the JC model groups closely related but distinct clades (Groups 1 and 2) into a single cluster with internal substructure. In contrast, the GTR model accounts for variable substitution rates across sites (i.e., it still allows different substitution rates between nucleotides), allowing finer resolution and clearer separation of these groups into distinct clades with stronger statistical support.

Fig. 1
figure 1

Global maximum likelihood phylogenetic tree of concatenated nucleotide sequences from 13 chromosomal markers of B. burgdorferi sensu stricto (BmpA, FlaB, oms66, P83-100, clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA, and the 16–23 S intergenic spacer). Isolates are color‑coded by geographic region: Manitoba (MB, blue), Ontario (ON, green), and Nova Scotia (NS, brown). Colored circle bands indicate genetic markers: ospC major groups (bold green), ribosomal sequence types (RST1, red; RST2, brown; RST3, light green), multilocus sequence types (STs, yellow), intergenic spacer (IGS) subtypes (purple), and ribosomal spacer patterns (RSPs, pink). Twelve numbered clades represent well‑supported phylogenetic groups and isolates not clustering within these monophyletic groups were annotated as singletons.

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Our results (Table S3) indicate that the Robinson-Foulds (RF) distance between the partitioned and concatenated models was minimal (RF distance = 10, normalized RF distance = 0.0769), suggesting strong topological consistency between both approaches. When benchmarked against the core genome phylogeny, the concatenated model achieved a lower normalized RF distance (0.2769) than the partitioned model (0.3385), indicating higher congruence with the core genome. Log-likelihood values were nearly identical between models (− 22557.732 versus − 22557.785), with ΔlogL = 0.052. However, likelihood-based tests - Shimodaira-Hasegawa (p-SH = 1.0 versus 0.0231), Kishino-Hasegawa (p-KH = 0.974 versus 0.0259), and approximate unbiased (p-AU = 0.976 versus 0.0235), strongly favored the concatenated model and statistically rejected the partitioned approach.

Taken together, these results support the validity of concatenation for phylogenetic reconstruction in B. burgdorferi s.s., demonstrating that a single evolutionary model across all genes (i.e., chromosomal genes) provides robust and reliable topologies comparable to partitioned approaches, while improving computational efficiency.

Phylogenetic tree comparison and congruence analysis

The core genome phylogenetic tree constructed in MEGA 5.2.237 with the Jukes-Cantor (JC) model revealed that concatenating sequences of bmpA, flaB, oms66, P83-100, eight housekeeping genes (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA), and the 16–23 S chromosomal marker, divided 59 of the 64 B. burgdorferi s.s. strains into 12 well-supported monophyletic groups that were also classified based on MLST, IGS, RSP, and RST markers. Group delineation was based either on the most recent common ancestor (MRCA) with high bootstrap support (≥ 0.95) or, in cases where a larger clade contained well-resolved subclades, on the consistency of those subclades with one or more independent typing methods (MLST, ospC MG, IGS, RSP, RST). The other five strains were singletons (Fig. 1). While higher-order branching patterns sometimes differed between core genome and plasmid-gene trees, the internal monophyly of each group was preserved across datasets. Branch lengths in the phylogenetic tree are proportional to nucleotide divergence, allowing inference of relative evolutionary distances among and within clades. These phylogenetic groups and their respective geographic distributions, based on the sequence types (STs) reported in the pubmlst.org database (Table S4), are as follows:

  • • Group 1 (ST55, IGS-1 A, RSP2, RST1): ST55 is found in the Midwestern USA (Minnesota, Wisconsin) and south central Canada (Manitoba, Ontario).

  • • Group 2 (ST1, IGS-1 A, RSP1, RST1): ST1 is predominantly distributed in the Northeastern USA (Connecticut, Massachusetts, Maine, New Hampshire, New York, Pennsylvania, Rhode Island, Virginia, Vermont) and central and southeastern Canada (Ontario, Quebec, and the Maritimes).

  • • Group 3 (ST46, IGS-8 C, RSP13, RST3): ST46 is found in the Midwestern USA (Wisconsin, Minnesota) and south central Canada (Ontario, Manitoba).

  • • Group 4 (ST51, ST268, IGS-5, RSP14): STs 51 and 268 are found in the Northeastern and upper Midwestern USA (Minnesota, Wisconsin, New York) and central and western Canada (Manitoba, British Columbia).

  • • Group 5 (ST3, IGS-2 A, RSP3, RST2): ST3 is primarily found in the Northeastern USA (Massachusetts, New York, Connecticut, Vermont, Virginia, Rhode Island, New Jersey) and southeastern Canada (Maritimes, Ontario, Quebec).

  • • Group 6 (ST4, ST32, ST740, IGS-2D, RSP4, RST2): STs 4, 32 and 740 are distributed across the Northeastern and Midwestern USA (Connecticut, Illinois, Massachusetts, Maine, Michigan, Minnesota, New Jersey, New York, Pennsylvania, Rhode Island, Wisconsin) and southern Canada (Manitoba, Ontario, Quebec, and the Maritimes).

  • • Group 7 (ST29, IGS-2D, RSP5, RST2): ST29 is found in the Northeastern and Midwestern USA (Connecticut, Illinois, Minnesota, Wisconsin) and across southern Canada (British Columbia, Manitoba, Ontario, Quebec).

  • • Group 8 (ST530, IGS-2D, RSP5, RST2): ST530 was identified in the upper Midwestern USA (Wisconsin, Minnesota) and south central Canada (Manitoba, Northwestern Ontario).

  • • Group 9 (ST16, ST227, ST237, ST741, IGS-7 A, RSP10, RST3): STs 16, 227, 237 and 741 are distributed across the Northeastern and upper Midwestern USA (Connecticut, Massachusetts, New York, Rhode Island, Wisconsin) and across southern Canada (British Columbia, Manitoba, Ontario, Quebec, and the Maritimes).

  • • Group 10 (ST12, ST221, IGS-6 A, RSP9, RST3): STs 12 and 221 are found in northeastern and upper Midwestern USA (Connecticut, Massachusetts, New York, Rhode Island, Wisconsin, Michigan, Illinois) and across southern Canada (British Columbia, Manitoba, Ontario, Quebec, and the Maritimes).

  • • Group 11 (ST43, IGS-5, RSP14, RST3): ST43 is found in the upper Midwestern USA (Wisconsin, Minnesota) and western and south central Canada (British Columbia, Manitoba, Northwestern Ontario).

  • • Group 12 (ST19, ST31, ST229, IGS9, RSP19, RST3): STs 19, 31 and 229 are distributed across the Northeastern and upper Midwestern USA (Connecticut, New York, US Midwest) and south central and southeastern Canada (Manitoba, Ontario, Quebec, and the Maritimes).

Notably, several core phylogenetic groups correspond to unique combinations of ST, IGS, and RSP types, such as Group 1 (ST55, IGS-1 A, RSP2, RST1), Group 2 (ST1, IGS-1 A, RSP1, RST1), and Group 5 (ST3, IGS-2 A, RSP3, RST3) (Table S3). This classification provides a framework for understanding the evolutionary relationships of B. burgdorferi s.s. strains. Seven strains had unique combinations of MLST, IGS, RSP, and RST types (Fig. 1).

Congruence across classification methods

To assess the consistency between core and plasmid-encoded genes, congruence was calculated between these core genome groups and the plasmid gene trees (Figures S3–S13) for ospC, dbpA, dbpB, oms28, ospA, ospB, ospD, Fibronectin P35, P37, P45-13 and C6 peptide of vlsE1 using different classification methods (Tables S5-S9). The tables provide a quantitative summary of the visual comparisons shown in Figures S3–S13, converting tree‑based congruence into a binary scoring system to facilitate direct comparison.

MLST classification

The MLST classification method consistently exhibited high congruence with the core genome tree across most phylogenetic groups. Notably, Group 1 (ST55), Group 2 (ST1), Group 3 (ST46), and Group 8 (ST530) achieved a high congruence (91%-100%) across all genes, indicating strong evolutionary coherence (Table S5). Fibronectin P35 showed high congruence (91%), indicating its stable alignment with the core genome. Similarly, dpbA, dpbB, and ospC also demonstrated high congruence (75%), reflecting their phylogenetic alignment with core genome markers. Conversely, ospA exhibited the lowest congruence (27%).

OspC major group classification

The major ospC grouping was variably congruent with the core genome and other plasmid markers. The C6 peptide of VlsE1 achieved the highest congruence (100%), followed by ospC (75%) and P45-13 (73%), indicating strong alignment between these genes and the core genome. However, Group 1 (ST55, ospC A) showed a lower congruence score (55%) compared to Group 2 (ST1, ospC A) at 73%, indicating differences in phylogenetic consistency between these groups. Genes like ospA (27%) and oms28 (42%) had lower congruence with ospC (Table S6).

IGS classification

The IGS classification method was poorly congruent with other markers. The C6 peptide showed the highest congruence with IGS (67%) across groups, particularly in Groups 3, 5, 8, 10 and 12. Other genes, such as Fibronectin P35 (18%) and ospA (18%), exhibited low congruence with core genome (Table S7).

RSP classification

The RSP classification method demonstrated moderate to high congruence with other markers. Groups 1, 3, and 8 RSP were > 78% congruent with ospD, C6, and P37, but RSP classification was poorly congruent with the ospA (36%) (Table S8).

RST classification

The RST classification method displayed the lowest overall congruence scores. dpbA, dpbB, oms28, ospC, and ospD showed minimal congruence with RST classification (respectively 17%, 33%, 8%, 17%, 25%), while others like ospA, ospB, Fibronectin P35, P45-13 and P37 exhibited no congruence (0%) (Table S9).

Group-level insights and general observations

Groups 1, 2, 3, and 8 showed the highest congruence scores across multiple classification methods, suggesting they form stable, monophyletic clades. Notably, Group 8 (ospC allele C3) exhibited high evolutionary stability across genes - but this is a very small group (only 2 strains). Conversely, Group 2 (ST1, ospC A) showed lower congruence compared to Group 1 (ST55, ospC allele A) (Fig. S3-S13).

Across all classification methods, MLST and ospC major groups displayed the highest overall congruence. In contrast, RST classification consistently showed the lowest congruence.

General insights across classification methods

The MLST and ospC major group classification methods demonstrated the highest overall congruence with the core genome. Genes such as, C6 peptide of vlsE1, and dbpA consistently showed high congruence across multiple methods (Fig. S3-S13). Conversely, ospA and P45-13 consistently exhibited lower congruence across all classification methods. The RST classification method demonstrated the lowest congruence across all genes.

Recombination versus mutation analysis

To understand the evolutionary forces shaping B. burgdorferi s.s. populations, we compared the impact of recombination versus mutation across the core genome and several plasmid-encoded genes using ClonalFrameML. The R/θ ratio, representing the relative contribution of recombination to mutation, revealed distinct patterns across different genomic regions. The core genome exhibited a moderately high R/θ ratio of 1.50 (Table 1), indicating that recombination plays a significant role in its evolution. However, while mutation contributes to genetic variation, its effects are generally more constrained compared to recombination, which can introduce larger-scale genomic changes. This relative constraint may help preserve the integrity of essential chromosomal regions.

Table 1 Estimates of the recombination-to-mutation ratio (R/θ) from clonalframeml for the core genome (BmpA, FlaB, oms66, P83-100, and eight MLST housekeeping genes) and 11 plasmid-encoded genes of Borrelia burgdorferi sensu stricto. Higher values indicate a greater contribution of recombination relative to mutation in shaping genetic diversity.
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Among the plasmid‑encoded antigens, recombination rates varied substantially: ospC showed the highest recombination relative to mutation (R/θ = 4.25), whereas ospA and P45‑13 exhibited markedly lower ratios, and ospB showed intermediate values (Table 1). These findings highlight the differential evolutionary pressures acting on major surface antigens.

Statistical analysis

Core genome and accessory gene relationships: To further investigate the degree of evolutionary consistency between core genome and accessory genes in B. burgdorferi s.s., we calculated the distance (as measure by % nucleotide divergence) of each gene, in every strain, to the homologue on the reference genome B31. We then checked for the strength of correlations in these distances between different pairs of genes (Table 2). Significant positive correlations were found between the C6 peptide and chromosomal genes: bmpA (r = 0.76, R² = 0.58, p < 0.001), flaB (r = 0.63, R² = 0.4, p < 0.001), and sequence types (ST) (r = 0.81, R² = 0.65, p < 0.001), IGS (r = 0.81, R² = 0.65, p < 0.001), RSP (r = 0.54, R² = 0.3, p = 0.003), and RST (r = 0.57, R² = 0.32, p = 0.002) (Table 2). Similarly, dbpA showed moderate positive correlations with bmpA (r = 0.6, R² = 0.35, p < 0.001), flaB (r = 0.55, R² = 0.31, p < 0.001), and ST (r = 0.59, R² = 0.34, p < 0.001). Negative correlations revealed potential evolutionary divergence. ospC showed negative correlations with oms66 (r = -0.39, R² = 0.15, p = 0.002) and P83-100 (r = -0.35, R² = 0.12, p = 0.005) (Table 2). Similarly, oms28 was negatively correlated with flaB (r = -0.3, R² = 0.09, p = 0.018).

Table 2 Spearman correlation analysis of similarity values to the Borrelia burgdorferi sensu stricto B31 reference genome for 11 plasmid-encoded genes and 8 core chromosomal genomic markers across 64 Canadian strains. Reported statistics include the coefficient of determination (R²), correlation coefficient (rho), and associated p-values (p) for each gene pair comparison.
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Hierarchical clustering analysis

To further examine how different genes reveal consistent patterns of sequence similarity to the B31 reference genome, hierarchical clustering analysis was performed using Euclidean distance and single-linkage methods. This analysis allowed us to visualize how the degree of similarity to the B31 reference genome translated into phylogenetic relationships among the core and accessory genomes. Strains carrying ospC alleles I (i.e., including Ia subtype) and K consistently formed monophyletic clades in both core and accessory genome trees, as confirmed by maximum likelihood (ML) phylogenetic trees with high bootstrap support values (1 for ospC I-Ia and 0.99 for ospC K) (Fig. S3). These clusters aligned with the positive correlations observed between ospC and markers like RSP (r = 0.47, R2 = 0.22, p < 0.001) and RST (r = 0.44, R2 = 0.19, p = 0.001) (Table 2).

However, negative correlations between ospC and core proteins such as oms66 were reflected in phylogenetic splits. The hierarchical clustering analysis revealed that strains carrying ospC I-Ia (Fig. 2A and B) were divided into two distinct clusters. A similar pattern was observed with ospC K (Fig. 2A and C), where the negative correlation with P83-100 resulted in a phylogenetic split.

Fig. 2
figure 2

Hierarchical dendrograms illustrating relationships between the plasmid gene ospC and selected chromosomal genomic markers in Borrelia burgdorferi sensu stricto. Panels show: (A) ospC vs. FlaB (positive correlation), (B) ospC vs. oms66 (negative correlation), and (C) ospC vs. P83-100 (negative correlation). The dendrograms include all 64 Canadian strains, with ospC molecular genotypes (MGs) labeled. Genotypes influenced by negative correlations are highlighted with red circles. The colors in the dendrogram denote distinct clusters.

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Degree of clonality and modularity of B. burgdorferi

To assess the degree of clonality in B. burgdorferi s.s. populations from different geographic regions (NS, ONMB), we compared the ratio of haplotypes to singletons. Ontario and Manitoba were combined due to the lack of significant statistic differences in their B. burgdorferi s.s. population clonality (data not shown). A Mann-Whitney U test revealed a statistically significant difference in clonality, measured as haplotype-to-singleton ratios, between the B. burgdorferi s.s. populations in Nova Scotia (NS) and the combined Ontario/Manitoba (ONMB) regions (U = 975, p < 0.001) showing that the degree of clonality is higher in NS compared to ONMB (Table 3).

Table 3 Results of the Mann–Whitney U test comparing haplotype-to-singleton ratios, based on clonal complexes defined by single-locus variants (SLVs), between Borrelia burgdorferi sensu stricto populations in Nova Scotia (NS) and combined Ontario/Manitoba (ONMB) regions. Cliff’s delta values are included to indicate effect size and direction of differences.
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Cliff’s Delta had a value of + 1, indicating all clonality ratios in NS were greater than those observed in ONMB, supporting the interpretation that the NS population demonstrates a consistently higher level of clonality relative to ONMB (Table 3).

Network analysis: To uncover regional patterns in population structure, we conducted network-based modularity and association analyses across the three regions: Nova Scotia (NS), Ontario (ON), and Manitoba (MB). In this analysis, we treated Ontario and Manitoba separately, unlike in the clonality analysis where they were grouped together. This distinction was made because clonality measures genetic redundancy within a population, whereas modularity assesses the presence of distinct genetic clusters (subpopulations) within a region. Since ON and MB share many strain types, it made sense to group them for clonality analysis. However, for modularity analysis, we observed regional genetic differentiation, with some groups being exclusive to Manitoba (Groups 3, 4, 7) and others to Ontario (Group 11), which warranted separate analyses.

The modularity index (Q) confirmed moderate to strong community structures across all regions. The Nova Scotia (NS) population had the highest modularity (Q = 0.68), indicating well-defined genetic clusters. In contrast, Ontario exhibited the lowest modularity (Q = 0.508), suggesting a more fragmented and mixed population structure, while Manitoba had an intermediate modularity value (Q = 0.634), reflecting moderate population structuring.

Beyond clustering, our network analysis highlighted clear genetic associations between B. burgdorferi s.s. strains in Nova Scotia, Ontario, and Manitoba and their respective reference strains, alongside shared genomic markers. Seven of the 12 monophyletic groups identified in Nova Scotia were closely related to reference strains, suggesting that these strains share a common evolutionary history while also displaying regional genetic divergence (Fig. 3). For example, Group 2 was genetically identical to the B31 reference strain, suggesting a direct lineage connection, while Group 5 showed similarities with B379 and 297, and Group 6 was closely related to the 156a strain. Group 12 exhibited genetic proximity to N40, reflecting a shared evolutionary background. The network analysis revealed distinct genetic associations between B. burgdorferi s.s. strains and reference strains across different regions, providing insights beyond core genome-based phylogenies. In Ontario, Groups 1, 8, 9, 10, and 11 aligned closely with reference strains 156a, ZS7, B331, 29,805, WI91-23, and N40 (Fig. 4), supported by genetic markers such as IGS-1 A (Group 1) and IGS-2D/RSP5 (Group 8). In Manitoba, nine of the twelve groups displayed distinct relationships, with Groups 6 and 7 associating with 94a, JD1, and 118a, while Groups 4 and 12 aligned with CA11-2 A and WI91-23 (Fig. 5). In Nova Scotia, Groups 2 and 5 were exclusive to this region, with Bb163 from Group 2 being genetically identical to the B31 reference strain. Notably, the same phylogenetic group sometimes aligned with different reference strains depending on the region; for instance, Group 1 aligned with 156a in Ontario but ZS7, 118a, and B331 in Manitoba. This network-based approach complements phylogenetic trees by incorporating gene-specific alignments, particularly for plasmid genes, which are not fully captured in core genome-based phylogenies.

Fig. 3
figure 3

Network graph illustrating genetic relationships among 26 Borrelia burgdorferi sensu stricto strains collected in Nova Scotia, Canada. Relationships are based on a comprehensive set of chromosomal and plasmid-encoded genomic markers. Chromosomal markers include BmpA (P39), FlaB (P41), oms66 (P66), and P83-100 (P83), together with eight housekeeping genes (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA) and the C6 peptide of VlsE1. Plasmid-encoded markers include dbpA (P17), dbpB (P18), fibronectin-binding protein (P35), oms28 (P28), ospA (P31), ospB (P34), ospC (MG), ospD (P30), P37, and P45-13 (P45). Strain nodes are color-coded by sampling location: yellow for Bedford, black for Lunenburg, red for Pictou, and green for Shelburne. Genomic marker nodes are shown in cyan. Black edges represent the highest sequence similarity scores, as determined by BLAST analysis, indicating genetic connections among strains based on shared markers.

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Fig. 4
figure 4

Network graph illustrating genetic relationships among 26 Borrelia burgdorferi sensu stricto strains collected in Ontario, Canada. Relationships are based on a comprehensive set of chromosomal and plasmid-encoded genomic markers. Chromosomal markers include BmpA (P39), FlaB (P41), oms66 (P66), and P83-100 (P83), together with eight housekeeping genes (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA) and the C6 peptide of VlsE1. Plasmid-encoded markers include dbpA (P17), dbpB (P18), fibronectin-binding protein (P35), oms28 (P28), ospA (P31), ospB (P34), ospC (MG), ospD (P30), P37, and P45-13 (P45). Strain nodes are color-coded by sampling location: green for Big Grassy, black for Big Island, brown for Birch Island, and blue for Manitou Rapids. Genomic marker nodes are shown in yellow. Orange edges represent the highest sequence similarity scores, as determined by BLAST analysis, indicating genetic connections among strains based on shared markers.

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Fig. 5
figure 5

Network graph illustrating genetic relationships among 26 Borrelia burgdorferi sensu stricto strains collected in Manitoba, Canada. Relationships are based on a comprehensive set of chromosomal and plasmid-encoded genomic markers. Chromosomal markers include BmpA (P39), FlaB (P41), oms66 (P66), and P83-100 (P83), together with eight housekeeping genes (clpA, clpX, nifS, pepX, pyrG, recG, rplB, uvrA) and the C6 peptide of VlsE1. Plasmid-encoded markers include dbpA (P17), dbpB (P18), fibronectin-binding protein (P35), oms28 (P28), ospA (P31), ospB (P34), ospC (MG), ospD (P30), P37, and P45-13 (P45). Strain nodes are color-coded by sampling location: green for Buffalo Point and blue for Roseau River. Genomic marker nodes are shown in peach. Brown edges represent the highest sequence similarity scores, as determined by BLAST analysis, indicating genetic connections among strains based on shared markers.

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Discussion

This study provides a comprehensive analysis of the genetic diversity and evolutionary dynamics of B. burgdorferi s.s. across three Canadian regions: Nova Scotia (NS), northwest Ontario (ON), and southeastern Manitoba (MB). Its northward expansion into Canada reflects recent introductions linked to I. scapularis range shifts31,54. Similar to U.S. populations, Canadian strains show complex region-specific trajectories shaped by multiple introductions, recombination, and local ecology29,55,56,57. Unlike B. bavariensis, which experienced a strong bottleneck58, B. burgdorferi s.s. in Canada exhibits a more complex population structure.

Our first objective was to explore the phylogenetic consistencies between chromosomal and plasmid-borne genes, and then how recombination and mutation contribute to the genetic structure of B. burgdorferi s.s. These comparisons inform on the reliability of these markers for typing and are relevant for understanding ecological determinants of diversity.

We identified 12 well-defined groups (excluding five singletons) supported by MLST, ospC, RSP, RST, and IGS markers, consistent with whole-genome phylogenies from Canada and the U.S. (e.g., Group 5 corresponds to Tyler et al.’s Clade K; Groups 1–2 reflect their Clade A subdivision)35. Statistical tests confirmed that concatenation of markers was appropriate. Interestingly, plasmid-encoded surface proteins and chromosomal genes showed unexpected phylogenetic concordance. This contrasts with European B. burgdorferi sensu lato species, where ospC often shows incongruence due to horizontal transfer across species (e.g., Fr-93-1 sharing ospC with B. finlandensis59. These observations indicate that interspecific introgression is a major evolutionary force shaping ospC diversity in Europe. Further study is needed to understand these differences.

Borrelia burgdorferi s.s groups

A literature review revealed consistent associations between genotypes, geography, and host ecology. Groups 7 and 9 occur broadly, while Groups 1, 3, and 8 occur in central regions (southern Canada/upper Midwest), and Groups 2 and 5 are found in the northeast. Group 11 is absent from eastern North America, and Groups 6 and 12 are not found west of the Rocky mountains16,17,19,20,60,61,62. Group 7 is widespread across reservoir hosts17,19,29,35,62. Group 2 (ST1) is associated with Peromyscus leucopus, and absent from much of central North America63,64. Group 6 (often carrying ospC K) is likely specialized for mice but can be reservoired by both P. maniculatus and P. leucopus18,61,65. Group 3 may be associated with chipmunk hosts as suggested by studies that associate ospC allele T with this species11,18,65.

Phylogenetic comparisons between core and accessory genomes

Our phylogenetic analysis revealed strong congruence between the core genome and specific plasmid-encoded genes within certain phylogenetic groups. Group 1 (ST55), Group 2 (ST1), Group 3 (ST46), and Group 8 (ST530) demonstrated 100% congruence between core genome markers and multiple plasmid genes. This high congruence aligns with findings from previous studies, which suggest that certain sequence types (STs) of B. burgdorferi s.s. exhibit strong genetic coherence due to shared evolutionary constraints and selection pressures acting on both core and accessory genomes56,66. The parallel inheritance of core and plasmid genes within these groups suggests that clonal expansion, rather than frequent recombination, plays a major role in shaping the genetic structure of these lineages. This supports previous evidence that recombination (or horizontal plasmid transfer) does not necessarily disrupt the alignment between core and accessory genomes67.

Margos et al. (2012) highlighted how geographic boundaries in the United States restrict genetic exchange between northeastern, midwestern, and western B. burgdorferi s.s. populations. Geographic barriers, reinforced by ecological factors, limit gene flow across regions. Strain-host associations may further contribute to this genetic isolation, if certain strains preferentially infect specific host species, creating additional ecological barriers to gene flow.

Margos et al. (2012) demonstrated that, geographically distinct strains, such as ST1 and ST55, exhibit unique clonal lineages while still sharing identical accessory genome elements, such as the ospC allele (A) Brisson and colleagues (2010) argued that northeastern and midwestern (B) burgdorferi s.s. populations share a common ancestor. Our findings support these studies as we identified significant divergence between Group 1 and Group 2, particularly regarding core genomic markers.

This underscores the role of geographic factors, including host and vector ecology20, in driving genetic differentiation within B. burgdorferi s.s. populations. Similar trends are documented in other bacterial species, such as Francisella tularensis68 and Yersinia pestis69, where spatially distinct populations adapt to local hosts, resulting in unique evolutionary trajectories.

Recombination versus mutation - evolutionary dynamics

To further understand the evolutionary forces shaping B. burgdorferi s.s., we evaluated the recombination and mutation rates in core and accessory genomes using ClonalFrameML. The R/θ ratio (recombination-to-mutation) was moderately high for the core genome (1.50), suggesting that recombination plays a significant role in its evolution, though not at a sufficient frequency to erode the phylogenetic signal.

Phylogenetic consistency and variability in recombination rates within the accessory genome can reflect adaptive evolutionary strategies in B. burgdorferi s.s. Specifically, ospC exhibited high R/θ ratios, and this gene is under diversifying selection driven by host adaptation responses70. Notably, ospA exhibited the lowest congruence with the core genome phylogeny, and a low recombination-to-mutation ratio. Unlike rapidly evolving genes that adapt to evade host immune responses, ospA’s specialized role in the tick vector (facilitating attachment to gut receptors essential for transmission) imposes unique evolutionary constraints71. These vector-specific pressures likely result in divergence from the core genome phylogeny despite minimal recombination, as observed in other vector-adaptive genes72.

In contrast, ospB, while also showing a low recombination-to-mutation ratio, exhibits moderate congruence with the core genome (i.e., reaching 75% of congruence reading the MLST classification method). ospB is expressed in both the tick and mammalian environments and plays a role in immune evasion, which exposes it to broader selective pressures across both ecological contexts73. These dual selective pressures likely drive ospB’s evolution in a way that partially aligns with the core genome, as it needs to retain functional integrity across diverse environments. P45-13, encoded by the bba57 gene74, shows similar evolutionary patterns to ospB with a low recombination-to-mutation ratio (0.08) and moderate congruence with the core genome. Like ospB, P45-13 fulfills roles in both the tick and mammalian environments, acting as a surface-exposed lipoprotein critical for establishing infection75.

Geographic structuring and population dynamics

Borrelia burgdorferi s.s. populations in Canada show strong geographic structuring. Nova Scotia strains were the most clonal (haplotype/singleton ratio = 4), reflecting limited recombination and gene flow, while Ontario/Manitoba strains were more diverse and interconnected. Modularity analysis confirmed compartmentalized clusters in NS versus higher connectivity in ON/MB, consistent with host/vector movement patterns19,28.

This pattern may be influenced by the movement of hosts or other environmental factors that facilitate greater genetic exchange between clusters in this region18. Seven of the 12 phylogenetic groups were recovered in network analysis, including distinct NS lineages such as Group 2 (B31-like) and Group 5 (close to 297). By contrast, ON/MB strains showed multiple interconnections, e.g., Group 1 linked to reference strains 156a (ON) and ZS7, 118a, B331 (MB). These results suggest stronger local adaptation in NS and higher genetic exchange in ON/MB18,19,76. This could suggest that these regions are geographically better connected, facilitating the movement of hosts and vectors (but see below), which would contribute to increased genetic exchange within B. burgdorferi s.s. populations28,54,77,78. The network analysis identified more interconnecting edges among groups in these regions, which suggests greater genetic exchange between strains and shared genomic components. For instance, Group 1 in Ontario aligns with the 156a strain, while in Manitoba, Group 1 shows genetic ties to ZS7, 118a, and B331 strains. This regional variation in genetic affinity is primarily observed in genes of the accessory genome, while the core genome remains consistent between Ontario and Manitoba, with the chromosome closely resembling ZS7. The connectivity between Groups 1 and 8 in MB, which share multiple markers like IGS-1 A and RSP2, indicates ongoing gene flow and shared evolutionary history, a phenomenon supported by earlier studies that reported high genetic exchange rates within B. burgdorferi s.s populations in interconnected landscapes56,79.

Further integrated field and laboratory studies are needed to explore exactly why these geographic differences exist. It is possible that the hills that run along the centre of NS limit connectivity amongst populations of ticks and B. burgdorferi s.s. that were likely founded by ticks introduced by migratory birds. Certainly, this could account for segregation of Pictou (which is on the north coast of NS) from the other sites where ticks were collected (which are on the south coast). However, there are no obvious geographical barriers (e.g. mountains or rivers) between the three sites on the south coast. Geographic separation of sites on the south coast of Nova Scotia (170 km from the Shelburne site to the Bedford site) is similar to that for the northeastern Ontario sites (approximately 110 km from Birch island to Manitou Rapids), but in the latter region, two of the sites are on islands in Lake of the Woods (Birch Island and Big Island) (see Fig. 1 in35. So connectivity amongst sites in both regions is most likely by migratory birds moving ticks and B. burgdorferi s.s. around. Perhaps another key factor distinguishing the ecology of B. burgdorferi s.s. in the two regions is the seasonal synchrony of activity of nymphal and larval I. scapularis in central regions of North America80. In northeastern North America, nymphal I. scapularis are mostly active in spring, while larvae are active in late summer, and this means northward migrating birds in spring carry almost exclusively nymphal I. scapularis, which moult into adult ticks that rarely feed on competent reservoir hosts. Consequently, although birds themselves can be infected and may occasionally transmit B. burgdorferi s.s. to larvae, dispersal driven primarily by spring-migrating birds (versus year-round dispersal by terrestrial hosts) creates a bottleneck to connectivity amongst B. burgdorferi s.s. populations in northeastern North America54. In contrast, in central regions spring migrating birds carry both nymphs and larvae, and larvae moult into nymphs that will feed on and (if infected) infect reservoir hosts. As a consequence introduction of B. burgdorferi s.s. into emerging I. scapularis populations appears to be much more efficient54, and we speculate that this permits greater connectivity of B. burgdorferi s.s. populations in this region.

Core and accessory genome co-evolution

Core–accessory co-evolution was evident. Positive correlations between vlsE1 (C6 peptide) and chromosomal genes (bmpA, flaB, MLST loci) suggest shared selective pressures linked to immune evasion and host colonization81,82. The IR6 region of vlsE1 is highly conserved within B. burgdorferi s.s83, making it a reliable diagnostic target, though it varies markedly across other Borrelia genospecies84.

In contrast, ospC showed negative correlations with core genes such as oms66 and P83-100, reflecting its high recombination and antigenic diversity, whereas these chromosomal genes remain conserved due to essential functional roles10,84,85,86,87.

Implications for Lyme disease epidemiology and diagnostics

The higher clonality observed in NS indicates a more genetically homogeneous population of B. burgdorferi s.s., which may simplify predictive modeling of spread and persistence. While genetic diversity could theoretically influence antigenic variability and diagnostic sensitivity, our study did not directly assess diagnostic performance, and current evidence from North America does not support a need for region-specific diagnostics87,88,89.

Our own unpublished exploratory serological studies in mice likewise did not reveal convincing differences in diagnostic performance across strains, although infection dynamics did vary by genotype89,90.

Geographic clustering of specific genotypes, such as the RST1/RSP1/ST1 clade associated with disseminated disease in NS, highlights the value of genomic surveillance for identifying and tracking potentially high-risk clades. This emphasizes the importance of continued monitoring of population structure to support public health strategies for early detection and intervention.

Conclusion

This study emphasizes the importance of considering both core and accessory genome components in understanding B. burgdorferi s.s. evolution. While the core genome provides a stable framework reflecting the evolutionary backbone of the bacterium, plasmid-encoded genes, particularly those involved in host-pathogen interactions, demonstrate substantial plasticity due to recombination events. This dynamic adaptability of the accessory genome enables B. burgdorferi s.s. to exploit a wide range of ecological niches and host environments, supporting its persistence across diverse geographic regions8,19.

Future studies should expand on these findings by increasing sample sizes and including broader geographic sampling across North America. Such efforts will help refine our understanding of the genetic landscape of B. burgdorferi s.s. and its evolutionary drivers, ultimately informing genomic surveillance and public health strategies for Lyme disease across different ecological regions.