Abstract
Mycobacterium bovis is the causal agent of bovine tuberculosis, one of the most important diseases currently facing the UK cattle industry. Here, we use high-density whole genome sequencing (WGS) in a defined sub-population of M. bovis in 145 cattle across 66 herd breakdowns to gain insights into local spread and persistence. We show that despite low divergence among isolates, WGS can in principle expose contributions of under-sampled host populations to M. bovis transmission. However, we demonstrate that in our data such a signal is due to molecular type switching, which had been previously undocumented for M. bovis. Isolates from farms with a known history of direct cattle movement between them did not show a statistical signal of higher genetic similarity. Despite an overall signal of genetic isolation by distance, genetic distances also showed no apparent relationship with spatial distance among affected farms over distances <5km. Using simulations, we find that even over the brief evolutionary timescale covered by our data, Bayesian phylogeographic approaches are feasible. Applying such approaches showed that M. bovis dispersal in this system is heterogeneous but slow overall, averaging 2km/year. These results confirm that widespread application of WGS to M. bovis will bring novel and important insights into the dynamics of M. bovis spread and persistence, but that the current questions most pertinent to control will be best addressed using approaches that more directly integrate WGS with additional epidemiological data.
Highlights
The increasing availability of bacterial whole-genome sequence (WGS) data makes it possible to generate sequence datasets for whole bacterial pathogen populations at high sampling densities
Average diversity for sequenced isolates from within the same herd breakdowns was low, with mean 0.69 single nucleotide polymorphism (SNP) and range 0–4 SNPs. This was considerably lower than the average minimum SNP differences between different Variable Number Tandem Repeat (VNTR)-10 breakdowns
This study suggests that it may be possible in principle to use WGS to identify under-sampled populations in M. bovis, in this case due to switching of VNTR-type between VNTR-10 and the closely related VNTR-1
Summary
The increasing availability of bacterial whole-genome sequence (WGS) data makes it possible to generate sequence datasets for whole bacterial pathogen populations at high sampling densities. Such comprehensive sequencing has yielded impressive advances in outbreak investigation (Eyre et al, 2013; Harris et al, 2010; Walker et al, 2012), and provided new insights into both spatial dissemination (Gray et al, 2011; Holden et al, 2013) and the complexities of multi-host pathogen systems (Mather et al, 2013; Viana et al, 2014).
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