All genospecies display a probable Asian origin
For both B. afzelii and B. garinii the oldest node
separates a clade containing only Asian isolates from a clade containing
isolates from both continents (Figure 2A,C) suggesting that the Asian
population is ancestral for both genospecies. Borrelia
bavariensis displays a deep branching between the two continents, and
European isolates are characterized by a low divergence and almost
clonal expansion as previously described (Becker et al., 2020; Gatzmann
et al., 2015) (Figure 2B). Our original analysis did not include RussianB. afzelii isolates. A single Russian B. afzelii isolate
exists with a full chromosome in GenBank, Tom3107 (Accession Number:
NZ_CP009212.1). We re-ran the phylogeny utilizing all B. afzeliichromosome sequences including Tom3107 and PBi as an outgroup to root
the tree (see Suppl. Met.). Tom3107 was basal to the monophyletic
European B. afzelii clade (Figure S1) suggesting a stepwise
colonization from far-east Asia through Russia into Europe, which was
not observed in the other two genospecies.
Higher genetic diversity (π (Nei, 1987)) was found in AsianB. bavariensis and B. garinii in comparison to their
European counterparts (Table 1). Genetic diversity was similar between
Asian and European B. afzelii isolates (Table 1). In all cases,
the Borrelia populations showed negative Tajima‘s D(Tajima, 1989) values (Table 1) as expected for bacteria due to the
influence of population expansion (Gatzmann et al., 2015; Tajima, 1989).
The European samples always showed more negative values (Table 2),
suggesting a more recent expansion into Europe. Borrelia
bavariensis displayed the largest difference in Tajima’s D and
also had the largest absolute divergence value
(Dxy (R. R. Hudson et al., 1992)) in comparison
to the other two genospecies hinting that B. bavariensisbranching is potentially the oldest and that B. afzelii is the
youngest with the lowest absolute divergence and difference in Tajima’sD (Table 1).
Our dataset, as with many others, includes non-randomly sampled isolates
which could lead to biased estimates of population level statistics
(Nei, 1987). As our data set includes randomly sampled isolates as well
(see Text S1 and Tables S3, S4, S5) we were able to test for potential
sampling biases. Interestingly, we did not observe strong bias in any of
these statistics (π , FST ,DXY , Tajima’s D ) when calculated on
datasets containing random and non-random samples (Text S2).