High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient.

Broman E, Bonaglia S, Norkko A, Creer S, Nascimento FJA

Mol. Ecol. 30 (13) 3023-3039 [2021-07-00; online 2020-08-19]

Benthic macrofauna is regularly used in monitoring programmes, however the vast majority of benthic eukaryotic biodiversity lies mostly in microscopic organisms, such as meiofauna (invertebrates < 1 mm) and protists, that rapidly responds to environmental change. These communities have traditionally been hard to sample and handle in the laboratory, but DNA sequencing has made such work less time consuming. While DNA sequencing captures both alive and dead organisms, environmental RNA (eRNA) better targets living organisms or organisms of recent origin in the environment. Here, we assessed the biodiversity of three known bioindicator microeukaryote groups (nematodes, foraminifera, and ciliates) in sediment samples collected at seven coastal sites along an organic carbon (OC) gradient. We aimed to investigate if eRNA shotgun sequencing can be used to simultaneously detect differences in (i) biodiversity of multiple microeukaryotic communities; and (ii) functional feeding traits of nematodes. Results showed that biodiversity was lower for nematodes and foraminifera in high OC (6.2%-6.9%), when compared to low OC sediments (1.2%-2.8%). Dissimilarity in community composition increased for all three groups between Low OC and High OC, as well as the classified feeding type of nematode genera (with more nonselective deposit feeders in high OC sediment). High relative abundant genera included nematode Sabatieria and foraminifera Elphidium in high OC, and Cryptocaryon-like ciliates in low OC sediments. Considering that future sequencing technologies are likely to decrease in cost, the use of eRNA shotgun sequencing to assess biodiversity of benthic microeukaryotes could be a powerful tool in recurring monitoring programmes.

Bioinformatics Compute and Storage [Service]

NGI Stockholm (Genomics Applications) [Service]

NGI Stockholm (Genomics Production) [Service]

National Genomics Infrastructure [Service]

PubMed 32706485

DOI 10.1111/mec.15561

Crossref 10.1111/mec.15561


Publications 6.6.6