Deciphering the role of host species for two Mycobacterium bovis genotypes from the European 3 clonal complex circulation within a cattle‐badger‐wild boar multihost system

Abstract

Bovine tuberculosis is a common disease affecting cattle and wildlife worldwide.Mycobacterium bovis circulation in wildlife decreases the efficacy of surveillance and control programs in cattle. Strains of the European 3 clonal complex are the most frequent in France. The aim of our work was hence to investigate the role played by cattle and wildlife species in the circulation of two M. bovis European 3 strains circulation. WGS of M. bovis strains collected between 2010 and 2017 in two distinct areas (Nouvelle-Aquitaine region, NAq, and Côte-d'Or département, CdO), from badgers, wild boars, and cattle were used in an evolutionary model to infer the transition between the three species. We computed host species transition and persistence between two consecutive nodes and the average number of transitions per tree. In total, 144 and 218 samples were collected respectively in CdO and NAq. In CdO, three between-species transition rates stood out: from cattle to badgers, from badgers to wild boars, and from wild boars to cattle. In NAq an additional fourth transition rate was identified: from badgers to cattle. However, host transition remained a rare event. Our results suggest that wild boars could be an intermediary host between badgers and cattle in the circulation of the studied strains in CdO and NAq. Our results also highlight the differences between these two areas, suggesting that the transition pattern does not only depend on the host species and other ecological, landscape and anthropic factors are important.

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

“…On the evolution of the M. bovis bacterium, we model substitutions as occurring randomly and independently at some prescribed rate (Eq (9)). While the range of substitution rates reflects that estimated in the literature [11–15], these estimates are often non-overlapping and suggest that the “true” substitution rate may vary in time (i.e. be a non-regular clock), and also vary depend on the pathogenesis of M. bovis and the immunological response to infection in specific hosts, as well as the specific lineage of M. bovis .…”

“…With the compounding effect of not all infections being detected by the tests available, this makes it unlikely that many “true” transmission pairs are fully characterised by these data, particularly between-species due to strong spatial and temporal biases in host sampling (see Supplementary Material, Fig S1). For these reasons we hesitate to make strong claims about “direction of transmission” from this analysis (as compared with analyses that build the broader phylogenetic tree [12, 15, 20]). Rather, this work indicates that WGS is highly effective for ruling out transmission pathways across sampled hosts, including instances when transmission appeared likely on the basis of other information (Fig S8B).…”

“…In an interval ∆t the expected number of SNPs to have been generated is m = µ∆t, with standard deviation m 2 − m 2 = √ µ∆t. M. bovis exhibits a slow rate of molecular evolution; the substitution rate µ is estimated across different studies to be within the range of µ ≈ [0.1−0.9] substitutions, per genome, per year [11][12][13][14][15].…”

Whole-Genome sequencing in routine Mycobacterium bovis epidemiology – scoping the potential

“…Recently, much effort has been dedicated to the use of WGS to inform on transmission dynamics within the M. bovis epi-system in a variety of international locations [12][13][14][15][16][17][18][19][20]. The specific goal in these studies has been to try to unpick the Gordian knot of the relative importance of intra-and inter-species transmission in complex, sympatric, multi-host livestock and wildlife populations.…”

“…The polytomies that result from such genetically homogeneous, short temporal window outbreak data can affect the robustness of the Bayesian phylogenetic trees used to infer transmission dynamics [20,22] and it is therefore accepted that in such settings genomic data from MTBC organisms can have relatively limited utility [23,24]. However, it is evident that when phylodynamic methods are successfully applied to M. bovis, this is best enabled by using multiple samples from the same locale, collected from multiple hosts over a wide temporal window of 10-30 years [12][13][14][15][16][17][18][19][20]. This facilitates detection of robust molecular clock behaviour, which is essential for inferring transmission dynamics [25,26].…”