Abstract
In recent years, the increased availability, efficiency and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), has significantly increased the amount of information we can use to study infectious diseases. Consequently, it has improved the precision of epidemiological inferences for pathogens like M. bovis. As a consequence, novel methods have been developed to combine spatial explicit data and pathogen’s genome to understand the impact of the landscape on epidemic spread in space and time (Dellicour et al., 2016).
In this study, we used WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. Ninety-one high-quality sequences were obtained from tissue samples collected in four abattoirs located in the North of the country (Egbe et al., 2017). Sixty-four of these had complete metadata including, information on the geolocation of the herd the cattle were coming from. We tested the maximum credibility trees generated with the software Beast 1.10.4 against several spatial explicit factors: land cover, road network, population and cattle density, altitude.
Our findings suggested that M. bovis in Cameroon is slowly expanding its epidemiological range over time, therefore endemic stability is unlikely, as it was previously hinted by other studies in the literature (Awah-Ndukum et al., 2012). The most recent common ancestor was dated in the 1950s, and after an initial slow spread, a sudden jump in space to cover the entire study area was dated during the late 1960s. As previously reported (Egbe et al., 2017), most of the M. bovis genetic diversity was detected in the Adamawa and North region. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of zoonotic transmission.
The spatial analysis suggested that percentage of land covered by forest and the altitude might have played a role in facilitating M. bovis spread in the area. While the former could be a proxy for wild species presence, the latter result could be due caused by the presence of agropastoral communities living in these regions uplands. Thanks to such rearing practices, cattle from different herds mingle together, increasing the number of potential infectious contacts, thus facilitating spread.
In conclusion, we showed how using genomic tools combined with geographical information can improve our understanding of livestock diseases ecology and spread patterns. This is of paramount importance in contexts where data about animal populations and their movements are scarce, which could hamper the usefulness of tools like epidemiological mechanistic models. Adopting genomic tools as part of surveillance would vastly improve our understanding of disease transmission processes and, therefore, control strategies.
References
Dellicour, S., Rose, R., Faria, N. R., Lemey, P., & Pybus, O. G. (2016). SERAPHIM: Studying environmental rasters and phylogenetically informed movements. Bioinformatics, 32(20), 3204–3206. https://doi.org/10.1093/bioinformatics/btw384
Egbe, N. F., Muwonge, A., Ndip, L., Kelly, R. F., Sander, M., Tanya, V., Ngwa, V. N., Handel, I. G., Novak, A., Ngandalo, R., Mazeri, S., Morgan, K. L., Asuquo, A., & Bronsvoort, B. M. D. C. (2017). Molecular epidemiology of Mycobacterium bovis in Cameroon. Scientific Reports, 7(1), 1–17. https://doi.org/10.1038/s41598-017-04230-6
Awah-Ndukum, J., Kudi, A. C., Bradley, G., Ane-Anyangwe, I., Titanji, V. P. K., Fon-Tebug, S., & Tchoumboue, J. (2012). Prevalence of bovine tuberculosis in cattle in the highlands of Cameroon based on the detection of lesions in slaughtered cattle and tuberculin skin tests of live cattle. Veterinarni Medicina, 57(2), 59–76. https://doi.org/10.17221/5252-VETMED