Novel transcriptome assembly and comparative toxicity pathway analysis in mahi-mahi (Coryphaena hippurus) embryos and larvae exposed to Deepwater Horizon oil

Abstract

The impacts of Deepwater Horizon (DWH) oil on morphology and function during embryonic development have been documented for a number of fish species, including the economically and ecologically important pelagic species, mahi-mahi (Coryphaena hippurus). However, further investigations on molecular events and pathways responsible for developmental toxicity have been largely restricted due to the limited molecular data available for this species. We sought to establish the de novo transcriptomic database from the embryos and larvae of mahi-mahi exposed to water accommodated fractions (HEWAFs) of two DWH oil types (weathered and source oil), in an effort to advance our understanding of the molecular aspects involved during specific toxicity responses. By high throughput sequencing (HTS), we obtained the first de novo transcriptome of mahi-mahi, with 60,842 assembled transcripts and 30,518 BLAST hits. Among them, 2,345 genes were significantly regulated in 96hpf larvae after exposure to weathered oil. With comparative analysis to a reference-transcriptome-guided approach on gene ontology and tox-pathways, we confirmed the novel approach effective for exploring tox-pathways in non-model species, and also identified a list of co-expressed genes as potential biomarkers which will provide information for the construction of an Adverse Outcome Pathway which could be useful in Ecological Risk Assessments.

Figure 1. Schematic flow diagram illustrating the workflow employed for sequencing, de novo assembling and annotating the mahi-mahi transcriptome and for determining changes in gene expression, GO terms and regulated pathways in DHW oil exposed mahi-mahi embryos and larvae.

Figure 2

Plot of normalized mean counts (expression) versus log2 fold change for untreated versus treated comparison (A). The X-axis plots normalized logCounts and the Y-axis is the log2 fold change (FC). Black dots represent non-significant genes, whereas red dots indicate significant differentially expressed genes (q < 0.05). (B) Venn diagram displaying the number of differentially expressed genes in 96 hpf larvae after slick oil exposure and the overlay between these gene lists identified by de novo and OnRamp approaches.

Figure 3. Heat maps clustering de novo (96h_slick) and OnRamp approaches (96h_slick, 96h_source, 48h_slick, 48h_source, 24h_slick, 24h_source) in canonical pathways and tox functions.

Orange indicates the z-score is positive. IPA predicts that the biological process or function is trending towards an increase when Z-scores ≥ 1.3. Blue indicates z-score is negative. IPA predicts that the biological process or function is trending towards a decrease when Z-scores ≤ −1.3.

Figure 4

Stacked Bar Charts display the number of up-regulated (red), down-regulated (green) in each tox-pathway as a percentage of the total number of dataset molecules overlapping with the Tox Lists by de novo (A) and OnRamp (B) approaches.

Figure 5

Heatmaps of genes in inhibited proliferation of liver cells (A), apoptosis of kidney cells (B) and hypertrophy of cardiac muscle (C) across different transcriptome analytic approaches, oil types and time. Heatmap colors: Red, up-regulated in the dataset; Green, down-regulated in the dataset; Gray; in the dataset but not analysis-ready (i.e. did not pass cutoffs and filters); White, not in the dataset. The orange or blue-colored squares in the column headers indicate the z-score (activity prediction) for each analysis. Orange shading indicates predicted activation and blue shading indicates predicted inhibition.

Figure 6

Top significant toxicity pathways through IPA-Tox showing how slick oil may impact/lead to (A) inhibition of liver proliferation; (B) cardiac hypertrophy; (C) apoptosis of kidney cells; (D) cholesterol biosynthesis.

Figure 7

A series of functions (e.g. muscle contraction, cell cycle regulation, CNS funtion, cholesterol biosynthesis and metabolism) pertubated through TR/RXR (A) and LXR/RXR (B) pathways predicted by IPA.