DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions

Abstract

DNA metabarcoding, especially when coupled with high-throughput DNA sequencing, is currently revolutionizing our capacity to assess biodiversity across a full range of taxa and habitats, from soil microbes (e.g., Thompson et al., 2017) to large marine fish (e.g., Thomsen et al., 2016), and from contemporary to tens of thousands year-old biological communities (e.g., Willerslev et al., 2003). The breadth of potential applications is immense and spans surveys on the diversity or diet of species native to specific ecosystems to bioindication (Pawlowski et al., 2018). The approach is also especially cost-effective and easy to implement, which makes DNA metabarcoding one of the tools of choice of the 21st century for fundamental research and the future of large-scale biodiversity monitoring programs (reviewed in Bohan et al., 2017; Creer et al., 2016; Taberlet, Bonin, Zinger, & Coissac, 2018; Thomsen & Willerslev, 2015). However, as is often the case with any emerging technology, we feel that the rise of DNA metabarcoding is occurring at a pace and in a manner that often loses sight of the challenges in producing high-quality and reproducible data (Baker, 2016). DNA metabarcoding is by essence a multidisciplinary approach building upon many complementary expertises, including field and theoretical knowledge, taxonomic expertise, molecular biology, bioinformatics, and computational statistics. Combining all these within single studies is necessary, not so much for producing and analyzing the data per se, but rather for minimizing and controlling the possible biases that can be introduced at any step of the experimental workflow—i.e., from the sampling to data analysis—and that can lead to spurious ecological conclusions (reviewed in Bálint et al., 2016; Nilsson et al., 2019; Dickie et al., 2018; Taberlet et al., 2018). Whether the starting material consists of DNA from bulk samples (community DNA) and/or from environmental DNA (eDNA), all DNA metabarcoding studies rely on a deceptively simple succession of core experimental steps: (a) sampling and preservation of the starting material, (b), DNA extraction, (c) PCR amplification of a taxonomically-informative genomic region, (d) high-throughput DNA sequencing of the amplicons, and (e) sequence analysis using bioinformatic pipelines. Despite this apparent simplicity, each step can potentially introduce its own sources of artifacts and biases (Figure 1). For example, the sampling design might not be effective for capturing the full taxonomic diversity or the ecological processes under study, an undesired bias for studies based on species detection. The availability of DNA in the samples is governed by its production rate, transport and persistence, processes which are all largely dependent on the targeted organisms, their biomass, and the ecosystem considered. A correct assessment of an ecological phenomenon based on DNA metabarcoding require not only implementation of standardized standardized, randomized and repeatable sampling designs and procedures (Dickie et al., 2018), but also consideration of DNA dynamics in the underlying matrix (i.e., in gut, faeces, water or soil matrices from tropical or boreal organisms/ecosystems; Barnes & Turner, 2016). Likewise, the community under study can be enriched on purpose or not with specific taxa depending on how the sample is collected (e.g., filter size for water samples, removal of roots or not for soils), how it is transported/preserved, and how DNA is extracted (differential extraction efficiencies). PCR amplification is also well known to be an important source of biases, that are now fully revealed with high-throughput DNA sequencing techniques. The preferential amplification of certain taxa over other ones due to inappropriate primers provides one such example of potential bias (Clarke, Soubrier, Weyrich, & Cooper, 2014; Deagle, Jarman, Coissac, Pompanon, & Taberlet, 2014). Primer biases can both skew abundance profiles and lead to false negatives. PCR amplification can produce false negatives too through the presence of e.g., PCR inhibitors, but also many false positives through the introduction of replication errors by the DNA polymerase or the formation of chimeric fragments (reviewed in Taberlet et al., 2018). False positives can also be introduced at any step of the experimental workflow through the presence of reagent contaminants (Salter et al., 2014), or through samples, extractions or PCR cross-contaminations. An even more insidious source of false positives pertains to the occurrence of “tag jumps”, sometimes referred to as “mistagging”, “tag-switching”, or “cross-talks” (Carlsen et al., 2012; Edgar, 2018; Esling, Lejzerowicz, & Pawlowski, 2015; Schnell, Bohmann, & Gilbert, 2015). PCR amplicons are indeed often tagged with unique short nucleotide sequences added on the 5’-end of the primers (i.e., “tags”), which allow pooling all PCRs within a single sequencing run and reducing sequencing costs. Each seque