Comparison between Transcriptome Sequencing and 16S Metagenomics for Detection of Bacterial Pathogens in Wildlife
2015
Razzauti Sanfeliu, Maria | Galan, Maxime | Bernard, Maria | Maman Haddad, Sarah | Klopp, Christophe | Charbonnel, Nathalie | Taussat, Muriel | Eloit, Marc | Cosson, Jean-Francois | Centre de Biologie pour la Gestion des Populations (UMR CBGP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [Occitanie])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Génétique Animale et Biologie Intégrative (GABI) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Génétique Physiologie et Systèmes d'Elevage (GenPhySE) ; Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Biologie moléculaire et immunologie parasitaires et fongiques (BIPAR) ; École nationale vétérinaire d'Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Laboratoire de santé animale, sites de Maisons-Alfort et de Normandie ; Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12) | PathoQuest SAS | Institut National de la Recherche Agronomique (INRA) | Découverte de Pathogenes ; Institut Pasteur [Paris] (IP) | MR received the support of the European Union, under the framework of the Marie-Curie FP7 COFUND People Program, through the award of an AgreenSkills fellowship (grant agreement n° 267196). JFC, MVT, MR, MG, MB, SM, CK, NC and ME received financial support from the PATHO-ID project funded by the meta-program Meta-omics des Ecosystems Microbiens (MEM) of the French National Institut for Agricultural Research (INRA). MVTand JFC were also supported by the COST Action TD1303 (EurNegVec). In addition JFC, NC, MG and MVTare funded by the EU grant FP7- 261504 EDENext and this study is catalogued by the EDENext Steering Committee as EDENext 355 (http://www.edenext.eu). | European Project: 261504
Background Rodents are major reservoirs of pathogens responsible for numerous zoonotic diseases in humans and livestock. Assessing their microbial diversity at both the individual and population level is crucial for monitoring endemic infections and revealing microbial association patterns within reservoirs. Recently, NGS approaches have been employed to characterize microbial communities of different ecosystems. Yet, their relative efficacy has not been assessed. Here, we compared two NGS approaches, RNA-Sequencing (RNA-Seq) and 16S-metagenomics, assessing their ability to survey neglected zoonotic bacteria in rodent populations.Methodology/Principal Findings : We first extracted nucleic acids from the spleens of 190 voles collected in France. RNA extracts were pooled, randomly retro-transcribed, then RNA-Seq was performed using HiSeq. Assembled bacterial sequences were assigned to the closest taxon registered in GenBank. DNA extracts were analyzed via a 16S-metagenomics approach using two sequencers: the 454 GS-FLX and the MiSeq. The V4 region of the gene coding for 16S rRNA was amplified for each sample using barcoded universal primers. Amplicons were multiplexed and processed on the distinct sequencers. The resulting datasets were de-multiplexed, and each read was processed through a pipeline to be taxonomically classified using the Ribosomal Database Project. Altogether, 45 pathogenic bacterial genera were detected. The bacteria identified by RNA-Seq were comparable to those detected by 16S-metagenomics approach processed with MiSeq (16S-MiSeq). In contrast, 21 of these pathogens went unnoticed when the 16S-metagenomics approach was processed via 454-pyrosequencing (16S-454). In addition, the 16S-metagenomics approaches revealed a high level of coinfection in bank voles. Conclusions/Significance :We concluded that RNA-Seq and 16S-MiSeq are equally sensitive in detecting bacteria. Although only the 16S-MiSeq method enabled identification of bacteria in each individual reservoir, with subsequent derivation of bacterial prevalence in host populations, and generation of intra-reservoir patterns of bacterial interactions. Lastly, the number of bacterial reads obtained with the 16S-MiSeq could be a good proxy for bacterial prevalence.
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