In two "départements" in the South-West of France, bovine tuberculosis (bTB) outbreaks due to Mycobacterium bovis spoligotype SB0821 have been identified in cattle since 2002 and in wildlife since 2013. Using whole genome sequencing, the aim of our study was to clarify badger contribution to bTB transmission in this area. We used a Bayesian evolutionary model, to infer phylogenetic trees and migration rates between two pathogen populations defined by their host-species. In order to account for sampling bias, sub-population structure was inferred using the marginal approximation of the structured coalescent (Mascot) implemented in BEAST2. We included 167 SB0821 strains (21 isolated from badgers and 146 from cattle) and identified 171 single nucleotide polymorphisms. We selected a HKY model and a strict molecular clock. We estimated a badger-to-cattle transition rate (median: 2.2 transitions/lineage/year) 52 times superior to the cattle-to-badger rate (median: 0.042 transitions/lineage/year). Using the maximum clade credibility tree, we identified that over 75% of the lineages from 1989 to 2000 were present in badgers. In addition, we calculated a median of 64 transition events from badger-to-cattle (IQR: 10-91) and a median of zero transition event from cattle-to-badger (IQR: 0-3). Our model enabled us to infer inter-species transitions but not intra-population transmission as in previous epidemiological studies, where relevant units were farms and badger social groups. Thus, while we could not confirm badgers as possible intermediaries in farm-to-farm transmission, badger-to-cattle transition rate was high and we confirmed long-term presence of M. bovis in the badger population in the South-West of France.
Cattle Diseases , Mycobacterium bovis , Tuberculosis, Bovine , Animals , Animals, Wild , Bayes Theorem , Cattle , Mycobacterium bovis/genetics , Phylogeny , Tuberculosis, Bovine/epidemiology , Tuberculosis, Bovine/microbiology
In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.
