Chromosome-Level Genome Assembly of the Viviparous Eelpout Zoarces viviparus.

The viviparous eelpout Zoarces viviparus is a common fish across the North Atlantic and has successfully colonized habitats across environmental gradients. Due to its wide distribution and predictable phenotypic responses to pollution, Z. viviparus is used as an ideal marine bioindicator organism and has been routinely sampled over decades by several countries to monitor marine environmental health. Additionally, this species is a promising model to study adaptive processes related to environmental change, specifically global warming. Here, we report the chromosome-level genome assembly of Z. viviparus, which has a size of 663 Mb and consists of 607 scaffolds (N50 = 26 Mb). The 24 largest represent the 24 chromosomes of the haploid Z. viviparus genome, which harbors 98% of the complete Benchmarking Universal Single-Copy Orthologues defined for ray-finned fish, indicating that the assembly is highly contiguous and complete. Comparative analyses between the Z. viviparus assembly and the chromosome-level genomes of two other eelpout species revealed a high synteny, but also an accumulation of repetitive elements in the Z. viviparus genome. Our reference genome will be an important resource enabling future in-depth genomic analyses of the effects of environmental change on this important bioindicator species.

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.

Introduction: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning. Methods: In this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods. Results: Sequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing. Discussion: We conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

Transcriptomic sequencing data illuminate insecticide-induced physiological stress mechanisms in aquatic non-target invertebrates.

Pesticides are major agricultural stressors for freshwater species. Exposure to pesticides can disrupt the biotic integrity of freshwater ecosystems and impair associated ecosystem functions. Unfortunately, physiological mechanisms through which pesticides affect aquatic organisms are largely unknown. For example, the widely-used insecticide chlorantraniliprole is supposed to be highly selective for target pest species, i.e. Lepidoptera (butterflies), but its effect in aquatic non-target taxa is poorly studied. Using RNA-sequencing data, we quantified the insecticide effect on three aquatic invertebrate species: the caddisfly Lepidostoma basale, the mayfly Ephemera danica and the amphipod Gammarus pulex. Further, we tested how the insecticide-induced transcriptional response is modulated by biotic interaction between the two leaf-shredding species L. basale and G. pulex. While G. pulex was only weakly affected by chlorantraniliprole exposure, we detected strong transcriptional responses in L. basale and E. danica, implying that the stressor receptors are conserved between the target taxon Lepidoptera and other insect groups. We found in both insect species evidence for alterations of the developmental program. If transcriptional changes in the developmental program induce alterations in emergence phenology, pronounced effects on food web dynamics in a cross-ecosystem context are expected.

Impacts of multiple anthropogenic stressors on the transcriptional response of Gammarus fossarum in a mesocosm field experiment.

BACKGROUND: Freshwaters are exposed to multiple anthropogenic stressors, leading to habitat degradation and biodiversity decline. In particular, agricultural stressors are known to result in decreased abundances and community shifts towards more tolerant taxa. However, the combined effects of stressors are difficult to predict as they can interact in complex ways, leading to enhanced (synergistic) or decreased (antagonistic) response patterns. Furthermore, stress responses may remain undetected if only the abundance changes in ecological experiments are considered, as organisms may have physiological protective pathways to counteract stressor effects. Therefore, we here used transcriptome-wide sequencing data to quantify single and combined effects of elevated fine sediment deposition, increased salinity and reduced flow velocity on the gene expression of the amphipod Gammarus fossarum in a mesocosm field experiment. RESULTS: Stressor exposure resulted in a strong transcriptional suppression of genes involved in metabolic and energy consuming cellular processes, indicating that G. fossarum responds to stressor exposure by directing energy to vitally essential processes. Treatments involving increased salinity induced by far the strongest transcriptional response, contrasting the observed abundance patterns where no effect was detected. Specifically, increased salinity induced the expression of detoxification enzymes and ion transporter genes, which control the membrane permeability of sodium, potassium or chloride. Stressor interactions at the physiological level were mainly antagonistic, such as the combined effect of increased fine sediment and reduced flow velocity. The compensation of the fine sediment induced effect by reduced flow velocity is in line with observations based on specimen abundance data. CONCLUSIONS: Our findings show that gene expression data provide new mechanistic insights in responses of freshwater organisms to multiple anthropogenic stressors. The assessment of stressor effects at the transcriptomic level and its integration with stressor effects at the level of specimen abundances significantly contribute to our understanding of multiple stressor effects in freshwater ecosystems.