16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife

0301 basic medicine Biodiversité et Ecologie http://aims.fao.org/aos/agrovoc/c_13948 bat L73 - Maladies des animaux molecular epidemiology 630 http://aims.fao.org/aos/agrovoc/c_16411 bacteria;emerging infectious diseases;high-throughput sequencing;metabarcoding;molecular epidemiology;rodents;West Africa;zoonoses Chiroptera maladie infectieuse http://aims.fao.org/aos/agrovoc/c_8355 bacteria Chordata surveillance épidémiologique http://aims.fao.org/aos/agrovoc/c_6970 0303 health sciences http://aims.fao.org/aos/agrovoc/c_2329 high-throughput sequencing Biodiversity QR1-502 3. Good health http://aims.fao.org/aos/agrovoc/c_5630 http://aims.fao.org/aos/agrovoc/c_0a9daef1 rodents Mammalia bactérie pathogène http://aims.fao.org/aos/agrovoc/c_34317 L72 - Organismes nuisibles des animaux agent pathogène Research Article zoonose 570 http://aims.fao.org/aos/agrovoc/c_28665 http://aims.fao.org/aos/agrovoc/c_10b87fa7 relation homme-faune bats http://aims.fao.org/aos/agrovoc/c_27813 Microbiology emerging infectious diseases Biodiversity and Ecology 03 medical and health sciences http://aims.fao.org/aos/agrovoc/c_15663 http://aims.fao.org/aos/agrovoc/c_34024 West Africa séquençage à haut débit Animalia http://aims.fao.org/aos/agrovoc/c_8530 rongeur Rattus L10 - Génétique et amélioration des animaux zoonoses transmission des maladies [SDE.BE] Environmental Sciences/Biodiversity and Ecology Enquête pathologique séquence d'arn metabarcoding next-generation sequencing [SDE.BE]Environmental Sciences/Biodiversity and Ecology
DOI: 10.1128/msystems.00032-16 Publication Date: 2016-07-20T02:04:09Z
ABSTRACT
Several recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. High-throughput sequencing (HTS) technologies are potentially useful for this surveillance, but rigorous experimental processes are required for the use of these effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw data set that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as positivity, prevalence, and coinfection, using 16S rRNA amplicon sequencing on an Illumina MiSeq platform. This procedure, applied to 711 rodents collected in West Africa, detected several zoonotic bacterial species, including some at high prevalence, despite their never before having been reported for West Africa. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (93)
CITATIONS (97)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....