Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data

Abstract

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Whole Genome Sequencing: Applications in Clinical Bacteriology

“…WGS was used to gain insights into the epidemiology of M. bovis in Cameroon. A total of 91 high-quality sequences of M. bovis were analyzed with respect to environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models [ 106 ]. The findings suggested that M. bovis in Cameroon was slowly expanding its epidemiological range over time; therefore, endemic stability was unlikely.…”

“…The findings suggested that M. bovis in Cameroon was slowly expanding its epidemiological range over time; therefore, endemic stability was unlikely. The authors concluded that the use of WGS as part of surveillance would vastly improve our understanding of disease ecology and control strategies [ 106 ].…”

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

“…WGS of M. bovis has so far been used to characterise clusters of transmission [20–24], and quantify rates of between-species transmission [12, 15, 20, 25–30]. Our interest here is in the more forensic problem of identifying “who-infected-whom” in a population of infected hosts, and whether WGS can be effectively employed to better discriminate between possible animal-to-animal transmission pathways.…”