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

Author:

Galan Maxime1,Razzauti Maria1,Bard Emilie2,Bernard Maria34,Brouat Carine5,Charbonnel Nathalie1,Dehne-Garcia Alexandre1,Loiseau Anne1,Tatard Caroline1,Tamisier Lucie1,Vayssier-Taussat Muriel6,Vignes Helene7,Cosson Jean-François16ORCID

Affiliation:

1. INRA, CBGP, Montferrier sur Lez, France

2. INRA, EpiA, Clermont-Ferrand, France

3. INRA, Sigenae, France

4. INRA, GABI, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France

5. IRD, CBGP, Montferrier sur Lez, France

6. INRA, Bipar, Maisons-Alfort, France

7. CIRAD, AGAP, Montpellier, France

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.

Funder

European Cooperation in Science and Technology

Agence Nationale de la Recherche

Institut National de la Recherche Agronomique

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modelling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

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