A Comparison between Transcriptome Sequencing and 16S Metagenomics for Detection of Bacterial Pathogens in Wildlife.
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.



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.



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.
INRA, UMR CBGP (INRA / IRD / Cirad / Montpellier SupAgro), Montpellier, France.
Maria
M
Razzauti
INRA, UMR CBGP (INRA / IRD / Cirad / Montpellier SupAgro), Montpellier, France.
Maxime
M
Galan
INRA, GABI, Domaine de Vilvert, Jouy-en-Josas, France.
Maria
M
Bernard
INRA, Sigenae Group, GenPhySE, INRA Auzeville, Castanet Tolosan, France.
Sarah
S
Maman
INRA, Sigenae Group, GenPhySE, INRA Auzeville, Castanet Tolosan, France.
Christophe
C
Klopp
INRA, UMR CBGP (INRA / IRD / Cirad / Montpellier SupAgro), Montpellier, France.
Nathalie
N
Charbonnel
INRA, UMR Bipar, ENVA, ANSES, USC INRA, Maisons-Alfort, France.
Muriel
M
Vayssier-Taussat
PathoQuest SAS, Paris, France; Ecole Nationale Vétérinaire d'Alfort, UMR 1161, Virologie ENVA, ANSES, INRA, Maisons-Alfort, France; Pasteur Institute, Laboratory of Pathogen Discovery, Biology of Infection Unit, Inserm U1117, Paris, France.
Marc
M
Eloit
INRA, UMR CBGP (INRA / IRD / Cirad / Montpellier SupAgro), Montpellier, France.
Jean-François
JF
Cosson
D000818:Animals; D000835:Animals, Wild / Q000382:microbiology*; D003411:Arvicolinae / Q000382:microbiology*; D001419:Bacteria / Q000145:classification / Q000235:genetics* / Q000302:isolation & purification*; D001424:Bacterial Infections / Q000382:microbiology / Q000662:veterinary*; D004269:DNA, Bacterial / Q000235:genetics*; D056186:Metagenomics; D008969:Molecular Sequence Data; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D059467:Transcriptome
D004269:DNA, Bacterial; D012336:RNA, Ribosomal, 16S