bioRxiv2016
ImportanceSeveral recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. Rodents are intermediate hosts for numerous zoonotic bacteria. High-throughput sequencing (HTS) technologies are very useful for the detection and surveillance of zoonotic bacteria, but rigorous experimental processes are required for the use of these cheap and effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw dataset that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as bacterial positivity, prevalence and coinfection, by 16S rRNA amplicon sequencing on the MiSeq platform. This procedure, applied to 711 commensal rodents collected from 24 villages in Senegal, Africa, detected several emerging bacterial genera, some in high prevalence, while never before reported for West Africa. This study constitutes a step towards the use of HTS to improve our understanding of the risk of zoonotic disease transmission posed by wildlife, by providing a new strategy for the use of HTS platforms to monitor both bacterial diversity and infection dynamics in wildlife. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.\n\nSummaryHuman impact on natural habitats is increasing the complexity of human-wildlife interfaces 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 of recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of large numbers of samples, 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 eliminate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a trimming strategy for managing them and cleaning the dataset. We also analyzed 711 commensal rodents collected from 24 villages in Senegal, including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis and 221 Mastomys erythroleucus. Seven major genera of pathogenic bacteria were detected: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus and Orientia. The last five of these genera have never before been detected in West African rodents. Bacterial prevalence ranged from 0% to 90%, depending on the bacterial taxon, rodent species and site considered, and a mean of 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, thus, particularly suitable for continuous pathogen surveillance in the framework of disease monitoring programs