Using metatranscriptomics to estimate the diversity and composition of zooplankton communities

Abstract

DNA metabarcoding is a rapid, high-resolution tool used for biomonitoring complex zooplankton communities. However, diversity estimates derived with this approach can be biased by the co-detection of sequences from environmental DNA (eDNA), nuclear-encoded mitochondrial (NUMT) pseudogene contamination, and taxonspecific PCR primer affinity differences. To avoid these methodological uncertainties, we tested the use of metatranscriptomics as an alternative approach for characterizing zooplankton communities. Specifically, we compared metatranscriptomics with PCR-based methods using genomic (gDNA) and complementary DNA (cDNA) amplicons, and morphology-based data for estimating species diversity and composition for both mock communities and field-collected samples. Mock community analyses showed that the use of gDNA mitochondrial cytochrome c oxidase I (mtCO1) amplicons inflates species richness due to the co-detection of extra-organismal eDNA.Significantly more amplicon sequence variants, nucleotide diversity, and indels were observed with gDNA amplicons than with cDNA, indicating the presence of putative NUMT pseudogenes. Moreover, PCR-based methods failed to detect the most abundant species in mock communities due to priming site mismatch. Overall, metatranscriptomics provided estimates of species richness and composition that closely resembled those derived from morphological data. The use of metatranscriptomics was further tested using field-collected samples, with the results showing consistent species diversity estimates among biological and technical replicates. Additionally, temporal zooplankton species composition changes could be monitored using different mitochondrial markers. These findings demonstrate the advantages of metatranscriptomics as an effective tool for monitoring diversity in zooplankton research.

Development of transcriptomics‐based growth rate indices in two model eukaryotes and relevance to metatranscriptomics datasets

“…However, not all species have the homolog genes of the nuclear gene proposed in this study, such as the homologue of ETHE1, which is yet to be discovered in S. cerevisiae. Lenz et al, 2021;Lopez et al, 2021;Machida et al, 2021).…”

Allometric scaling of RNA abundance from genes to communities

“…In this study, we validated the assumptions of the MTE and GRH on the allometric scaling of mRNA transcript and ribosome abundance using constructed mock communities. Using mock communities has been an efficient tool for molecular-based laboratory and computational experiments, leading to thorough quantitative and qualitative validation of new methods (10).…”

“…Metatranscriptomics is a sequencing tool that uses NGS to detect and quantify RNA in a community sample at a given time point (10). Based on the MTE-GRH model (8), transcription rate (TR), which refers to mRNA and rRNA production per unit of time, is expected to vary with body size and temperature:…”

MIDORI2: A collection of quality controlled, preformatted, and regularly updated reference databases for taxonomic assignment of eukaryotic mitochondrial sequences

“…Conserved primers are available for PCR‐based surveys of selected metazoans, all metazoans, or all eukaryotes [e.g., Cytochrome c oxidase subunit 1 ( CO1 ) (Elbrecht et al, 2019; Leray et al, 2013); Small rRNA ( srRNA ) (Machida et al, 2012); Large rRNA ( lrRNA ) (Kelly et al, 2016)]. Studies have also successfully used PCR‐free mitochondrial metagenomics (mitogenomics) and metatranscriptomics to infer composition and relative biomass of complex mixed‐species samples (Bista et al, 2018; Lopez et al, 2022; Machida et al, 2021).…”

“…Studies have also successfully used PCR-free mitochondrial metagenomics (mitogenomics) and metatranscriptomics to infer composition and relative biomass of complex mixed-species samples (Bista et al, 2018;Lopez et al, 2022;Machida et al, 2021).…”