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The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals using High Throughput Transcriptomics and Benchmark Concentration Modeling

Ramaiahgari SC, Auerbach SS, Saddler TO, Rice JR, Dunlap PE, Sipes NS, DeVito MJ, Shah RR, Bushel PR, Merrick BA, Paules RS, Ferguson SS

Toxicological Sciences (2019) DOI: https://doi.org/10.1093/toxsci/kfz065 PMID: 30850835
CEBS DOI: https://doi.org/10.22427/NTP-DATA-002-00058-0001-0000-6
SRA No. SRP166108; BioProject No. PRJNA497448

Publication

Abstract

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.

Figures

Figure 1 A-D. Concentration-response plots of S1500+ in 2D-DIFF HepaRG cultures using BMDExpress 2.2

A, Gene-level accumulation plots of 24 test chemicals used in the study.
B, Gene-level accumulation plots of trovafloxacin and levofloxacin.
C, Gene level-accumulation plots of troglitazone and rosiglitazone. Read counts from the TempO-Seq high-throughput transcriptomic analysis of 2978 transcripts were normalized, and benchmark concentration accumulation plots were derived using BMExpress 2.2. X-axis represents the range of exposure concentrations examined, with each data point reflecting respective derived benchmark concentrations, in nanomolar units, and y-axis represents benchmark concentration accumulation (integer accumulation for each BMC). Data shown from one of 3 independent experimental runs and were representative of trends for each run.
D, Lactate dehydrogenase (LDH) leakage data of 2D-DIFF HepaRG cells exposed to various chemicals for 96 h. X-axis represents concentration of the compound in micromolar units, and y-axis represents relative fluorescence units (RFU). Data are representative of 3 independent runs.

Figure 2 A-C. Cell culture configuration impacts on hepatocyte functionality

A, Hierarchical clustering of genes associated with hepatocyte differentiation and xenobiotic metabolism pathways in 2D-DIFF and PROLIF HepaRG cultures; heatmap and clustering of genes were performed on Partek Genomics Suite 6.6.
B, Omeprazole induced CYP1A1 gene induction in 2D-DIFF (blue) and PROLIF (black); x-axis represents concentration of omeprazole in micromolar, and y-axis represents average mapped read counts (TempO-Seq) of 3 replicates, and its standard deviation (SD).
C, Omeprazole induced CYP3A4 gene induction in 2D-DIFF (blue) and PROLIF (black); x-axis represents concentration of omeprazole in nanomolar, and y-axis represents average mapped read count (TempO-Seq) of 3 replicates ± SD. Data are representative of 3 independent runs.

Figure 3 A-B. Hepatic nuclear receptor pathway functionality in differentiated (2D-DIFF) HepaRG

A, Activation of classical nuclear receptor signaling pathways AhR, CAR, PXR, PPARα, and FXR in 2D-DIFF HepaRG, −log(p-value) > 1.3 (dotted line) represents significant activation (Ingenuity Pathway Analysis).
B, Representative gene-level activation of hepatic nuclear receptors pathways with prototypical inducers omeprazole (AhR, CYP1A1); phenobarbital (CAR, CYP2B6); rifampicin (PXR, CYP3A4); fenofibric acid (PPARα, CYP4A22); chenodeoxycholic acid (FXR, SLC51β). Data are representative of 3 independent runs.

Figure 4 A-E. The power of concentration-response toxicogenomics.

A, Heatmap of approximately 500 pathway (Ingenuity Pathway Analysis, IPA) responses of 24 chemicals calculated by comparing fold-change values (> 2-fold) to their respective vehicle controls. −log(p-value) was displayed in the heatmap; −log(p-value) > 1.3 (p-value = .05) was considered significantly altered pathway upon chemical exposure in 2D-DIFF HepaRG. Zoomed heatmap shows unsupervised clustering of compounds at their respective exposures that led to cell cycle and DNA damage responses. *Exposure concentrations with observed cytotoxicity were removed from the analysis.
B, BMDExpress 2.2 visualization of GO biological processes in concentration-response. Stress signaling pathways associated with DNA damage and cell cycle with aflatoxin B1 and benzo(a)pyrene exposures are shown in the plot. X-axis represents concentrations of chemical in nanomolar, Y-axis represents accumulation rank of pathway-level benchmark concentrations.
C, Hierarchical clustering of canonical pathway changes in 2D-DIFF and PROLIF HepaRG with aflatoxin B1 and benzo(a)pyrene exposures. –log(p-value) are displayed in the heatmap; −log(p-value) > 1.3 (p-value = .05) considered significantly altered. Zoomed heatmap shows unsupervised clustering of differentially regulated pathways in 2D-DIFF and PROLIF HepaRG. Data are representative of three independent runs.
D, Representative photomicrographs of vehicle control cultures for 2D-DIFF (upper panel) and PROLIF (lower panel) HepaRG at the beginning and end of the compound exposure period, and with 1.5 µM aflatoxin b1.
E, Accumulation plots from BMDExpress 2.2 showing cyclophosphamide induced Gene Ontology (GO) biological processes in 2D-DIFF and PROLIF HepaRG with increasing concentration. X-axis represents the range of exposure concentrations examined, with each data point reflecting respective derived median benchmark concentrations, in nanomolar units, and y-axis represents benchmark concentration accumulation (integer accumulation for each BMC).

Figure 5 A-B. High-throughput transcriptomic analyses showing pathway-level benchmark concentration (BMC

A, Pathway analysis of menadione-associated Gene Ontology (GO) biological processes in 2D-DIFF HepaRG.
B, Pathway analysis of trovafloxacin and levofloxacin exposure in 2D-DIFF and PROLIF HepaRG. Accumulation plots were generated from BMDExpress 2.2. X-axis represents the range of exposure concentrations examined, with each data point reflecting respective derived median BMCs, in nanomolar units, and y-axis represents BMC accumulation (integer accumulation for each BMC).

Figure 6 A-C. High-throughput transcriptomic analysis with BMDExpress 2.2 identifies biological-response

A, PPAR-gamma pathway perturbation with exposures to troglitazone and rosiglitazone across concentration response in 3 independent runs. X-axis represents compound exposure concentrations in nanomolar units, and y-axis represents benchmark concentration accumulation (integer accumulation for each BMC).
B, Toxicological pathways associated with troglitazone and rosiglitazone exposure in 2D-DIFF HepaRG across 3 independent runs. X-axis represents the range of exposure concentrations examined, with each data point reflecting respective derived median BMCs, in nanomolar units, and y-axis represents BMC accumulation (integer accumulation for each BMC).
C, Expression levels of individual transcripts associated with PPARγ pathway in troglitazone- and rosiglitazone-exposed 2D-DIFF HepaRG. X-axis represents the range of exposure concentrations examined, with each data point reflecting respective derived median BMCs, in nanomolar units, y-axis represents log2 transformed mapped read count.

Additional Materials

Supplementary Data

FASTQ Files

The SRA fastq data files for this study (SRA study number SRP166108) are available for download from the NCBI SRA website using the SRA Toolkit. Links to the Toolkit and supporting documentation are provided below. Details describing configuration, fastq file download, and other functions in Toolkit are available in the Installation and Configuration Guide. The application is run in the command prompt from the directory containing the Toolkit.exe file and does not have a user-interface. Instructions and descriptions of the commands used by Toolkit are given in the Toolkit Documentation,

SRA Toolkit: Download
Toolkit Installation and Configuration Guide: Download
Toolkit documentation: Download

Raw Read Count Data

SRA Metadata

BMD Analysis Files

BMDExpress data (24 chemical HepaRG BMD Analysis File) and
installation files (BMDExpress2_2_for_Winddows_64bit.zip)

Cell Morphology and Cytotoxicity Assessments

LDH Data