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

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.

Foliar fungal communities strongly differ between habitat patches in a landscape mosaic

“…The negative controls contained 29,857 fungal sequences clustering into 337 OTUs. There is no consensus on how to deal with OTUs found in negative controls (Nguyen et al, 2015; Galan et al, 2016). It is difficult to distinguish real contaminations (sequences originating from the people who performed the experiments, the laboratory environment and the DNA extraction kit) from cross-contaminations between samples, occurring during the DNA extraction, amplification and sequencing (Esling, Lejzerowicz & Pawlowski, 2015; Galan et al, 2016).…”

“…There is no consensus on how to deal with OTUs found in negative controls (Nguyen et al, 2015; Galan et al, 2016). It is difficult to distinguish real contaminations (sequences originating from the people who performed the experiments, the laboratory environment and the DNA extraction kit) from cross-contaminations between samples, occurring during the DNA extraction, amplification and sequencing (Esling, Lejzerowicz & Pawlowski, 2015; Galan et al, 2016). It is highly probable that OTUs assigned to Erysiphe alphitoides , the agent responsible for the oak powdery mildew (1.5% of the negative control sequences; Jakuschkin et al, 2016) or Botrytis cinerea , responsible for the grey mold on grapes (1.2%; Jaspers et al, 2016) are likely cross-contaminations because they are strongly related to a specific host.…”

Invasive Drosophila suzukii facilitates Drosophila melanogaster infestation and sour rot outbreaks in the vineyards

“…These wild flies were used to identify whole body-associated bacteria by high-throughput sequencing of a DNA amplicon coding for the 16S ribosome RNA gene. We used universal primers to amplify a 251-bp portion of the V4 region of the 16S rRNA gene (16S-V4F:587 GTGCCAGCMGCCGCGGTAA; 16S-V4R: GGACTACHVGGGTWTCTAATCC) and a dual-index method to multiplex our samples [17,18]. Laboratory preparation for DNA extraction, PCRs (in duplicate), and library preparation was performed as in [18].…”

Genotyping ofBartonellabacteria and their animal hosts: current status and perspectives

“…In their paper, Razzauti et al (2015) have also reported an interesting observation about a large difference in the relative abundance of Bartonella reads detected by the 16S MiSeq (95%) vs RNA sequencing (<1%). Galan et al (2016) investigated the potential for recent developments in 16S rRNA-based highthroughput sequencing (Illumina MiSeq) to facilitate the multiplexing of urban rodents in West Africa. This study reported significant difference in Bartonella prevalence between rodent species varying from 0·5% in Mus musculus to 79% in Mastomys natalensis.…”

“…This study reported significant difference in Bartonella prevalence between rodent species varying from 0·5% in Mus musculus to 79% in Mastomys natalensis. Praising advances in this screening strategy, the authors admit that 16S rRNA amplicon sequencing based on a short sequence did not yield results sufficiently high in resolution to distinguish between Bartonella species (Galan et al 2016). Another metagenomic evaluation of bacteria in voles from Finland (Koskela et al 2017) reported commonality of Bartonella species in the voles, although identification of the species was not clear.…”