Impacts of the Husky oil spill on gut microbiota of native fish species to the North Saskatchewan River

GeoNetwork record:www.gwfnet.net/geonetwork/srv/eng/catalog.search#/metadata/b3f6ea7a-3ce2-4eb0-be67-932d03f4b432
Tracking ID under eDNA project: UofS-eDNA-dataset-metadata-4

These data are collected from goldeye (Hiodon alosoides), walleye (Sander vitreus), northern pike (Esox lucius), and shorthead redhorse (Moxostoma macrolepidotum) from the North Saskatchewan River in 2017, following the Husky oil spill. The samples (gut contents) were sterilely collected from the guts of each fish and stored at -80C until DNA extraction. The guts were used for 16S rRNA sequencing to analyze differences in the microbiome among species as well as the impact of the Husky oil spill on gut microbial communities. Each fish was analyzed independently (samples were not pooled). The results of this study showed a significant effect of host species on the gut microbiome as well as marginal effects of the Husky oil spill.

The gut microbiota of animals has been described as an additional host ‘organ' with beneficial roles. However, little is known about the impact of chemical exposures on the structure and function of gut microbiota of fishes. In July 2016, a Husky Energy pipeline spilled 225,000 liters of diluted bitumen (dilbit), with much of the dilbit entering the North Saskatchewan River near Maidstone, SK. This event provided a unique opportunity to assess the shifts of gut microbiota in native fish species following exposure to dilbit. In summer 2017, goldeye (Hiodon alosoides), walleye (Sander vitreus), and shorthead redhorse (Moxostoma macrolepidotum) were collected at six locations upstream and downstream of the spill, and in summer 2018, these same species were collected at four sites. Muscle and bile were sampled from each fish for chemical measurements, and intestinal contents were collected for gut microbial analyses. Gut contents from these species were also collected at additional sites within the province as controls for the North Saskatchewan River. The purpose of this study was to determine if PAHs were still present in these fish and whether the gut microbiome of these fish was altered resulting from this spill.

This study sought to determine the effects of exposure resulting from an oil spill on the gut microbial communities of native fish species. An oil spill occurred on the North Saskatchewan River in July 2016, providing an opportunity to study the effects of oil on the gut microbiome in fish. The gut microbiome is a relatively new area of research in toxicology. It is believed that several toxicant can impair the gut bacteria and result in decreased health of an organism, which is why this study wanted to look at how oil spills impacts the gut bacteria.

DeBofsky, A., Xie, Y., Jardine, T., Jones, P., Giesy, J. (2020). Impacts of the Husky oil spill on gut microbiota of native fish species to the North Saskatchewan River [Dataset]. Federated Research Data Repository. https://doi.org/10.20383/101.0255

DeBofsky, A., Xie, Y., Jardine, T. D., Hill, J. E., Jones, P. D., Giesy, J. P. (2020). Effects of the husky oil spill on gut microbiota of native fishes in the North Saskatchewan River, Canada. Aquatic Toxicology 229: 105658. https://doi.org/10.1016/j.aquatox.2020.105658

North Saskatchewan River

Tracking ID under eDNA project: UofS-eDNA-dataset-metadata-4

Sample Collection
Fish were collected in gill nets set overnight in the North Saskatchewan River. Intestinal samples were collected from fish from each site. All samples were collected with sterile dissection tools from the bottom 1/3 of the intestines, with separate samples collected for gut contents and tissue. Samples were immediately placed on ice prior to being transferred to a -80C freezer for long-term storage.
16s amplicon sequencing
Total genomic DNA was extracted from guts using the DNeasy PowerSoil Kit (Qiagen Inc., Mississauga, ON). Concentrations were measured using a Qubit 4 Fluorometer and dsDNA HS assay kit (ThermoFisher Scientific, Waltham, MA). The V3-V4 variable region of the 16S rRNA gene was amplified using the Bact-0341 forward primer (CCTACGGGNGGCWGCAG) (Klindworth et al., 2013) and the Bact-806 reverse primer (GGACTACNVGGGTWTCTAAT) (Apprill et al., 2015). Samples were dual indexed to increase throughput of sequencing (Fadrosh et al., 2014). Samples were amplified with a 50 μL PCR reaction including Phusion green polymerase (ThermoFisher Scientific) using a SimpliAmp thermal cycler (ThermoFisher Scientific) under the following conditions: initial denaturation at 98°C for 30s, followed by 25 cycles of 98°C for 30s, 58°C for 30s, and 72°C for 30s, with a final extension at 72°C for 10 min. PCR products were assessed for size and specificity using electrophoresis on a 1.2% w/v agarose gel and purified using the Qiagen QIAquick PCR Purification Kit (Qiagen Inc.). All purified products were quantified with the Qubit dsDNA HS assay kit and concentrations were adjusted to 1 ng/ μL with molecular-grade water. Purified products were pooled, and libraries were constructed using the NEBNext® DNA Library Prep Master Mix Set for Illumina® (New England BioLabs Inc., Whitby, ON). Libraries were quantified prior to sequencing using the NEBNext® Library Quant Kit for Illumina®. Sequencing was performed on an Illumina® MiSeq instrument (Illumina, San Diego, CA) using a 2x300 base pair kit.

Data processing
Sequences were trimmed, cleaned, and demultiplexed using a combination of Trimmomatic (Bolger et al., 2014), USEARCH v11 (Edgar 2010), and QIIME1 (Caporaso et al., 2010). Paired-end sequences were merged with DADA2 (Callahan et al., 2016) in QIIME2 (Bolyen et al., 2019) after truncating the forward read to 280 nucleotides and the reverse read to 230 nucleotides in order to ensure maximum quality and percentage of reads retained. The DADA2 package generates sequence variants (SVs) that are used to infer different bacterial species. Chimeric sequences were subsequently removed, and SVs were compared to the Silva rRNA database release 132 for taxonomic identification in QIIME2. Samples were rarefied to a sequencing depth of 10,533 reads prior to downstream analyses. Statistical analyses were performed in PRIMER-e v7 (Auckland, NZ) and R (R Core Team, 2013).

Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Data can be downloaded from: https://doi.org/10.20383/101.0255