Genome-wide identification of molecular pathways and biomarkers in response to arsenic exposure in zebrafish liver

Abstract

Inorganic arsenic is a worldwide metalloid pollutant in environment. Although extensive studies on arsenic-induced toxicity have been conducted using in vivo and in vitro models, the exact molecular mechanism of arsenate toxicity remains elusive. Here, the RNA-SAGE (serial analysis of gene expression) sequencing technology was used to analyse hepatic response to arsenic exposure at the transcriptome level. Based on more than 12 million SAGE tags mapped to zebrafish genes, 1,444 differentially expressed genes (750 up-regulated and 694 down-regulated) were identified from a relatively abundant transcripts (>10 TPM [transcripts per million]) based on minimal two-fold change. By gene ontology analyses, these differentially expressed genes were significantly enriched in several major biological processes including oxidation reduction, translation, iron ion transport, cell redox, homeostasis, etc. Accordingly, the main pathways disturbed include metabolic pathways, proteasome, oxidative phosphorylation, cancer, etc. Ingenity Pathway Analysis further revealed a network with four important upstream factors or hub genes, including Jun, Kras, APoE and Nr2f2. The network indicated apparent molecular events involved in oxidative stress, carcinogenesis, and metabolism. In order to identify potential biomarker genes for arsenic exposure, 27 out of 29 up-regulated transcripts were validated by RT-qPCR analysis in pooled RNA samples. Among these, 14 transcripts were further confirmed for up-regulation by a lower dosage of arsenic in majority of individual zebrafish. Finally, at least four of these genes, frh3 (ferrintin H3), mgst1 (microsomal glutathione S-transferase-like), cmbl (carboxymethylenebutenolidase homolog) and slc40a1 (solute carrier family 40 [iron-regulated transporter], member 1) could be confirmed in individual medaka fish similarly treated by arsenic; thus, these four genes might be robust arsenic biomarkers across species. Thus, our work represents the first comprehensive investigation of molecular mechanism of asenic toxicity and genome-wide search for potential biomarkers for arsenic exposure.

Figure 1. Comparison of transcriptomic profiles between arsenic-treated and control groups.

(A) Distribution of transcript entries and total transcript counts in both arsenic-treated and control groups. The percentages of accumulated transcript counts or transcript entries are plotted over different transcript abundance categories. (B) Plot of transcript change fold (Y-axis) versus transcript TPM (X-axis) after arsenic exposure. Both axes are in log2 scale and TPM in the X-axis is based on the treatment group.

Figure 2. Diseases inferred by IPA based on differentially expressed genes after arsenic exposure.

The bar chart shows the number of arsenic-deregulated genes matched in different disease or disorder categories and only top significant categories (P<0.01) were selected to show. The same gene may be assigned to more than one categories.

Figure 3. Key upstream regulator networks modulated by arsenic exposure.

The upstream network was generated by IPA and the networks indicate predicted upstream regulators and their downstream target genes presented in the differentially expressed gene set. Up-regulated genes are in red and down-regulated in green. Solid arrow lines represent direct interaction while dotted lines indirect intereaction.

Figure 4. Preliminary identification of potential biomarker genes for arsenic exposure.

Selected up-regulated genes by arsenic exposure were examined by RT-qPCR in individual zebrafish (A) and medaka (B) after treatment with arsenic. (A), Fold changes (log2 ratio) of 14 up-regulated genes measured by RT-qPCR. S1-S9, 9 individual zebrafish treated with 15 ppm sodium; 15 ppm, average of the 9 individual fish; 20 ppm, RT-qPCR measurement from the pooled RNA sample used for RNA-SAGE sequencing; SAGE, RNA-SAGE data for comparison (Log2 fold change). The 9 genes displayed dosage-dependent effect between 15 ppm and 20 ppm are indicated with asterisks. Zebrafish gene symbols and names are shown based on NCBI and underlined genes are annotated manually. (B), Average of fold changes (log2 ratio) of four validated medaka genes in 4 individual medaka fish. (C) Comparison of the expression of arsenic biomarker genes in other chemical treatments by hierarchical clustering heatmap. RNA-SAGE data from the current study (Arsenic) were compared with hepatic RNA-SAGE data from zebrafish treated with 5 µg/L 17β-estradiol (E2), 5 µg/L 11-keto testosterone (KT11) or 10 nM 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). The left clustering is based on the 14 genes identified from zebrafish and the right based on the four genes from both zebrafish and medaka.