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. 2023 Dec 13;24(24):17425.
doi: 10.3390/ijms242417425.

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model

Venkat R Pannala  1   2 , Anders Wallqvist  1
Affiliations

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model

Venkat R Pannala et al. Int J Mol Sci. .

Erratum in

Abstract

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

Keywords: dose response; gene expression; high-throughput transcriptomics; histopathology; liver toxicity.

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Conflict of interest statement

V.R.P. is employed by The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Figures

Figure 1
Figure 1
Cluster diagram and Pearson’s correlation coefficients of all genes across various doses and chemical exposures. (A) Hierarchical clustering of logarithmic fold-change (FC) values of all genes irrespective of their significance levels. Here, the columns represent the individual dose levels of each non-hepatotoxic and hepatotoxic chemical (name in bold font). Red and green indicate genes that are up- and downregulated, respectively. For clarity, concentrations of each chemical are shown as sequential numbers, with actual concentration values listed in Table 1. (B) Pairwise Pearson’s correlation coefficients of altered gene expression changes (independent of their significance levels) across all the dose ranges for the non-hepatotoxic and hepatotoxic chemicals.
Figure 2
Figure 2
Genes with significant alterations and cluster diagram of genes that are common across highly toxic chemicals. (A) A summary of the total number of genes that changed significantly (false discovery rate < 0.1) at the highest concentration of each chemical and the common genes for their pairwise combinations. Here, the values in bold indicate the chemicals with the largest perturbations. (B) Hierarchical clustering of logarithmic fold-change (FC) values of significantly altered genes that are common for the highly toxic chemicals (indicated in bold font) and were monitored across all 18 chemicals. Red and green indicate genes that were up- and downregulated, respectively.
Figure 3
Figure 3
Cluster diagrams of alterations in the KEGG pathways related to amino acid metabolism. Hierarchical clustering of individual subpathways in amino acid metabolism for both non-hepatotoxic chemicals and hepatotoxic chemicals. Red and green indicate pathways that are up- and downregulated, respectively.
Figure 4
Figure 4
Cluster diagrams of alterations in the KEGG pathways related to lipid metabolism. Hierarchical clustering of individual subpathways in lipid metabolism for both non-hepatotoxic and hepatotoxic chemicals. Red and green indicate pathways that are up- and downregulated, respectively.
Figure 5
Figure 5
Summary of the liver histopathological outcomes for all 18 chemicals. (A) An exemplar summary of the dose–response behavior of injury module activation scores for the chemical fenofibrate (FEN). Green shaded region indicates the dose levels for which the z-score values are significant (p < 0.05). (B) An overall summary of the liver injury module predictions of the histopathological outcomes for all 18 chemicals classified as non-toxic, non-hepatotoxic, and hepatotoxic. We indicate a module is activated with the color red, and this is consistent for each chemical if the z-score values are greater than 2 and are significant with p-values less than 0.05 for at least two consecutive dose levels (Supplementary Table S4). If the z-score values are greater than 2 and significant for low dose levels but not significant at high dose levels (p-value > 0.05), then the module activation is considered inconsistent (striped bar).
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