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. 2018 Oct 30:9:485.
doi: 10.3389/fgene.2018.00485. eCollection 2018.

A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples

Pierre R Bushel  1 , Richard S Paules  2 , Scott S Auerbach  2
Affiliations

A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples

Pierre R Bushel et al. Front Genet. .

Abstract

The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.

Keywords: RNA-Seq; S1500+; TempO-Seq; chemicals; microarray; mode of action; toxicants; toxicogenomics.

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Figures

Figure 1
Figure 1
Study design. The study comprised of gene expression data acquired from male Sprague-Dawley rats dosed once daily in triplicate for 3, 5, or 7 days depending on the test chemical or matched control, and livers were harvested 24 h after the last dose. The abbreviations for the names of the chemicals are listed in Table 1. There were five modes of action (MOAs) with three chemicals per MOA. The MOAs are PPARA, peroxisome proliferator-activated receptor alpha; CAR/PXR, orphan nuclear hormone receptors; AhR, aryl hydrocarbon receptor; Cytotoxic, cytotoxicity, and DNA Damage. Comparisons between the data from TempO-Seq to microarray and RNA-Seq were performed by statistical and bioinformatics methodologies.
Figure 2
Figure 2
Variance components explained. Shown on the y-axis is the weighted average of the proportion of variance explained by platform for each of the mixed effect linear model terms denoted in the x-axis.
Figure 3
Figure 3
Gene expression patterns with maximal signal to noise. For each platform, the EPIG pattern with the maximal signal to noise ratio (SNR) is shown. The y-axis is the log2 ratio of gene expression (treated to the average of the control [matched according to nutritional status of the vehicle]), the x-axis is the samples grouped by MOA (represented by the colors and symbols in the legend). The table inset displays the magnitude of fold change, the noise and the SNR for each of the patterns shown.
Figure 4
Figure 4
Principal component analysis of the data. (A) Microarray. (B) RNA-Seq. (C) TempO-Seq. PCA performed using the log2 ratio expression data (treated to matched control according to nutritional status) of the genes that vary by MOA at FDR < 0.01.
Figure 5
Figure 5
Clustering of data. (A) Microarray. (B) RNA-Seq. (C) TempO-Seq. Clustering performed using the log2 ratio expression data (treated to matched control according to nutritional status) of the genes that vary by MOA at FDR < 0.01 with cosine correlation as the similarity metric and the Ward clustering criterion. The data for clustering was standardized to a mean of 0 and standard deviation of 1. Samples' MOA colored as in the legend to Figure 4A.
Figure 6
Figure 6
Clustering of the data using a common gene set. (A) Hierarchical clustering performed using the log2 ratio expression data (treated to matched control according to nutritional status) of the genes that vary by MOA at FDR < 0.01 and map to 731 UniGene cluster IDs that overlap between the three platforms. Genes that were mapped to the same UniGene cluster ID were averaged. The cosine correlation was used as the similarity metric and the Ward clustering criterion for merging clusters. Samples' MOA colored as in the legend to Figure 4A. Platforms are represented by the following colors: pink, Affymetrix; light blue, RNA-Seq; yellow, TempO-Seq. (B) PCA of the data used in (A). Principal component (PC) #1 = 34%, PC #2 = 16.6 %, and PC #3 = 9.53.
Figure 7
Figure 7
Comparison of enriched GO biological processes (BPs). (A) Overlap of enriched GO BPs < FDR 5%. Minimum number of genes = 3 for TempO-Seq and 5 for the other two. (B) Pairwise comparison of GO BPs fold enrichment from the 49 categories in common between the three platforms. Red line is the linear fit (regression line) with 95% level confidence boundaries.
Figure 8
Figure 8
Clustering of enriched GO BPs. goSTAG clustering of 203 enriched GO BPs using 5 genes per category, BH FDR < 0.05, correlation distance (1-Pearson correlation) and Ward clustering, dendrogram threshold = 0.9 and minimum number of GO BP terms per cluster = 5. Data is the –log10 p-value. The more red the intensity, the more significant the enrichment. Gray indicates that the GO BP term was not enriched significant and thus the p-value was imputed with 1.0.
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References

    1. Barrett T., Wilhite S. E., Ledoux P., Evangelista C., Kim I. F., Tomashevsky M., et al. (2013). NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 41, D991–D995. 10.1093/nar/gks1193 - DOI - PMC - PubMed
    1. Bates D., Machler M., Bolker B., Walker S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. 10.18637/jss.v067.i01 - DOI
    1. Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. SerB 57, 289–300.
    1. Bennett B. D., Bushel P. R. (2017). goSTAG: gene ontology subtrees to tag and annotate genes within a set. Source Code Biol. Med. 12:6. 10.1186/s13029-017-0066-1 - DOI - PMC - PubMed
    1. Chou J. W., Zhou T., Kaufmann W. K., Paules R. S., Bushel P. R. (2007). Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes. BMC Bioinformatics 8:427. 10.1186/1471-2105-8-427 - DOI - PMC - PubMed