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

• DOI: 10.1128/msystems.00032-16
• Publication Date: 2016-07-20T02:04:09Z
• Authors: Maxime Galan; Maria Razzauti; Emilie Bard; Maria Bernard; Carine Brouat; Nathalie Charbonnel; Alexandre Dehne-G...; Anne Loiseau; Caroline Tatard; Lucie Tamisier; Muriel Vayssier-T...; Helene Vignes; Jean-François Cosson
• Keywords: 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

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.