Résumé
The human impact on natural habitats is increasing the complexity of
human-wildlife interactions and leading to the emergence of infectious
diseases worldwide. Highly successful synanthropic wildlife species, such
as rodents, will undoubtedly play an increasingly important role in
transmitting zoonotic diseases. We investigated the potential for recent
developments in 16S rRNA amplicon sequencing to facilitate the
multiplexing of the large numbers of samples needed to improve our
understanding of the risk of zoonotic disease transmission posed by urban
rodents in West Africa. In addition to listing pathogenic bacteria in wild
populations, as in other high-throughput sequencing (HTS) studies, our
approach can estimate essential parameters for studies of zoonotic risk,
such as prevalence and patterns of coinfection within individual hosts.
However, the estimation of these parameters requires cleaning of the raw
data to mitigate the biases generated by HTS methods. We present here an
extensive review of these biases and of their consequences, and we propose
a comprehensive trimming strategy for managing these biases. We
demonstrated the application of this strategy using 711 commensal rodents,
including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys
natalensis, and 221 Mastomys erythroleucus, collected from 24 villages in
Senegal. Seven major genera of pathogenic bacteria were detected in their
spleens: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia,
Streptobacillus, and Orientia. Mycoplasma, Ehrlichia, Rickettsia,
Streptobacillus, and Orientia have never before been detected in West
African rodents. Bacterial prevalence ranged from 0% to 90% of individuals
per site, depending on the bacterial taxon, rodent species, and site
considered, and 26% of rodents displayed coinfection. The 16S rRNA
amplicon sequencing strategy presented here has the advantage over other
molecular surveillance tools of dealing with a large spectrum of bacterial
pathogens without requiring assumptions about their presence in the
samples. This approach is therefore particularly suitable to continuous
pathogen surveillance in the context of disease-monitoring programs.