Beyond taxonomy: Validating functional inference approaches in the context of fish-farm impact assessments.

Olivier Laroche,Xavier Pochon,Nigel Keeley,Susanna A Wood

doi:10.1111/1755-0998.13426

Olivier Laroche, Xavier Pochon + Show 2 more

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https://doi.org/10.1111/1755-0998.13426

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Abstract

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

Highlights

Aquatic biomonitoring has drastically changed in the last decade with the advent of high throughput sequencing (HTS) and the substantial cost reduction of sequencing (Deiner et al, 2017; Lobo et al, 2017; Ruppert et al, 2019; Thomsen & Willerslev, 2015; Valentini et al, 2016; Wang et al, 2019)
Metagenomics, defined as the study of the entire DNA material recovered from environmental samples, and metatranscriptomics, as the study of gene expression from mRNA recovered from environmental samples, have received considerable attention in the last few years (Bourlat et al, 2013; Grossart et al, 2020; Knapik et al, 2019; Semmouri et al, 2020)
Our main objectives were to assess the level of correspondence between the taxonomic and functional profiles derived from amplicon-based 16S ribosomal RNA (rRNA) metabarcoding data and shotgun metagenomics, evaluate the strengths and weaknesses of both approaches, and determine whether functional profiles from hidden-state prediction (HSP) methods can be used as a substitute to metagenomics for monitoring functional changes associated with fish farming in marine environments

Summary

Introduction

Aquatic biomonitoring has drastically changed in the last decade with the advent of high throughput sequencing (HTS) and the substantial cost reduction of sequencing (Deiner et al, 2017; Lobo et al, 2017; Ruppert et al, 2019; Thomsen & Willerslev, 2015; Valentini et al, 2016; Wang et al, 2019). While amplicon-based eDNA metabarcoding provides information on which organisms are present, metagenomics and metatranscriptomics enable insights into the functions they possess, and in the latter instance, the activity of these functions. This is relevant when microorganisms are being used as indicator organisms. Taxonomic and functional profiles can respond differently to biogeography, abiotic environmental variables (e.g., organic content, metal concentration) and community processes and interactions As such, they can exhibit different level of stochasticity and temporality, and provide complementary information that may increase our understanding of the mechanisms behind community turnover (Barberan et al, 2012; Cordier et al, 2020; Hornick & Buschmann, 2018; Louca et al, 2016). Having both taxonomic and functional information enables the computation of functional redundancy within the community, which may help assess resilience (Escalas et al, 2019)

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