Transmission patterns of a Mycobacterium avium subsp. paratuberculosis clone within a single heard investigated by Whole Genome Sequencing

•Phylogenetic analysis disclosed the within-herd population structure of MAP.•Bayesian phylodynamic inference was feasible despite a low substitution rate.•Multiple isolates recovered from a single animal originate from different donors. Mycobacterium avium subsp. paratuberculosis (MAP) is characterized by a low genomic rate of mutation. Current subtyping tools, such as Mini-Micro-satellite analyses, do to have not sufficient discriminatory power to disclose MAP’s evolution on small spatial and temporal scales. The aim of the study was to investigate the population structure of MAP inside a single dairy herd using whole genome sequencing (WGS) approaches. For this purpose, the genomes of 43 field isolates, recovered from the faeces of 36 cows of the same dairy herd from 2012 to 2016, were sequenced by WGS. The isolates’ genomes showed a low number (43) of polymorphic sites (SNPs), confirming the clonal origin of the herd infection. However, despite the limited genomic diversity found in WGS, the phylogenetic analysis was discriminatory enough to detect the presence of different genomic clades and sub-clades inside the herd population. In addition, the phylodynamic reconstruction showed the existence of an ancestor clade from which the other clades and sub-clades originated. Moreover, by reconstructing the putative within-herd transmission networks using WGS data, we demonstrated that: (i) in a herd where MAP is endemic, multiple isolates recovered from a single animal and differing from each other by few (three/four) SNPs can originate from different transmission or passive shedding events and not from intra-host evolution; and (ii) variability of minisatellites coupled with a few microsatellites does not represent reliable tracers of within-herd infection chains. Our findings show that WGS, coupled with relevant epidemiological information, represents a valuable tool to work out fine epidemiological and micro-evolutionary relationships such as those at herd-level scale.