Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

Abstract

Gene expression profiling is a useful tool to predict and interrogate mechanisms of toxicity. RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for conducting transcriptional profiling. The objective of this work was to compare both transcriptomic platforms to determine whether RNA-Seq offered significant advantages over microarrays for toxicogenomic studies. RNA samples from the livers of rats treated for 5 days with five tool hepatotoxicants (α-naphthylisothiocyanate/ANIT, carbon tetrachloride/CCl₄, methylenedianiline/MDA, acetaminophen/APAP, and diclofenac/DCLF) were analyzed with both gene expression platforms (RNA-Seq and microarray). Data were compared to determine any potential added scientific (i.e., better biological or toxicological insight) value offered by RNA-Seq compared to microarrays. RNA-Seq identified more differentially expressed protein-coding genes and provided a wider quantitative range of expression level changes when compared to microarrays. Both platforms identified a larger number of differentially expressed genes (DEGs) in livers of rats treated with ANIT, MDA, and CCl₄ compared to APAP and DCLF, in agreement with the severity of histopathological findings. Approximately 78% of DEGs identified with microarrays overlapped with RNA-Seq data, with a Spearman's correlation of 0.7 to 0.83. Consistent with the mechanisms of toxicity of ANIT, APAP, MDA and CCl₄, both platforms identified dysregulation of liver relevant pathways such as Nrf2, cholesterol biosynthesis, eiF2, hepatic cholestasis, glutathione and LPS/IL-1 mediated RXR inhibition. RNA-Seq data showed additional DEGs that not only significantly enriched these pathways, but also suggested modulation of additional liver relevant pathways. In addition, RNA-Seq enabled the identification of non-coding DEGs that offer a potential for improved mechanistic clarity. Overall, these results indicate that RNA-Seq is an acceptable alternative platform to microarrays for rat toxicogenomic studies with several advantages. Because of its wider dynamic range as well as its ability to identify a larger number of DEGs, RNA-Seq may generate more insight into mechanisms of toxicity. However, more extensive reference data will be necessary to fully leverage these additional RNA-Seq data, especially for non-coding sequences.

Keywords: DEG; IPA; RNA-Seq; in vivo; liver toxicity; microarray; non-coding transcripts; toxicogenomics.

FIGURE 1

(A) Principal component analysis (PCA) of the RNA-seq and microarray dataset for 26 liver samples. (1) Two-component PCA for Microarray dataset of ANIT, MDA, and CCL₄ (left) (2) RNA-Seq dataset of ANIT, MDA, and CCl₄ (right). Percentages represent variance captured by each principal components 1 and 2 in each analysis. Controls are shown in green color circle and hepatotoxicants are colored differently. (B) Principal component analysis of APAP and DCLF liver samples (1) two-component analysis of microarray data for APAP and DCLF (left). The beige color represents water treated control samples. The red colored samples are DCLF treated. The light and dark green circles represent corn oil control and APAP treated samples respectively. (2) RNA-Seq data analysis on APAP and DCLF (right). The drug treated samples are shown within the closed circle or oval shaped ring.

FIGURE 2

Overall computational process of RNA-Seq and microarray data analysis. 26 liver samples (15 drug treated and 11 controls) were assessed by microarray and RNA-Seq platform. Comparison at raw expression, differentially expression and pathway stages are indicated. A statistical criteria of p < 0.01 and FC < –1.5 or FC > 1.5 were used to obtain DEGs from raw expression data.

FIGURE 3

Scatter plot showing the relative expression levels of genes in terms of log₂FCs for 18,776 consensus genes, determined by RNA-Seq and microarray. Log₂FC is computed by taking average of three samples. Blue indicates RNA-Seq’s down-regulated and red is up-regulated protein coding genes. The graphs show that the overall FC dynamic ranges (log2 transformed) for 18,776 genes.

FIGURE 4

Concordance of protein-coding DEGs from RNA-Seq and Microarray. Blue and green bars indicate total number of microarray platform identified DEGs and number of inter-platform overlapping DEGs, respectively.

FIGURE 5

Spearman’s correlation plot for DEGs determined by RNA-Seq and microarray. Size of the filled circle is proportional to fold change difference (i.e., the larger the circle the bigger the FC difference). The blue and red spheres indicate down and upregulated DEGs, respectively.

FIGURE 6

Hierarchically clustered genes (columns) and samples (rows) with dendrograms and clusters (blue colored bars). Red in the heatmap denotes upregulation while blue denotes downregulation.

FIGURE 7

Volcano plot summarizing RNA-Seq specific non-coding DEGs. The red dots on the right top quadrant are significantly up-regulated non-coding DEGs and the dots within the top left quadrant shows highly down-regulated non-coding DEGs. Green color dots denote un-changed non-coding transcripts.