Abstract
Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the underlying mechanisms of adverse effects of xenobiotics. In the present study, two widely used human in vitro hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis or necrosis. Benchmark concentration-response modelling was applied to transcriptomics gene co-expression networks (modules) in order to derive benchmark concentrations (BMCs) and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in deregulated genes and modules by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics point of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100 fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs, that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and used in a risk assessment when BMCs are paired with chemical exposure assessment.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated introduction paragraph on gene co-expression modules and the TXG-MAP tool; extended the method description of the transcriptomics data analysis steps; added PHH cytotoxicity data; added connection of CSA and cholestasis and VPA and steatosis; included or explained often used abbreviations; changed the discussion on BHT metabolism by CYP2B1 for rat and CYP2B6 for human; clarified the discussion on the potential application of module level BMC to determine tPOD and conection to KEs in AOPs; Supplementary figures and tables revised and renumbered when needed.
https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12668
https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12677
Abbreviations
- AOP
- Adverse outcome pathway
- ATF4 / 6
- Activating transcription factor 4 / 6
- APAP
- Acetaminophen
- BHT
- Butylated hydroxytoluene
- BMC
- Benchmark concentration
- BMD
- Benchmark dose
- Cmax
- Maximum concentration
- CRGs
- Concentration responsive genes
- CSA
- Cyclosporine A
- CYP
- Cytochrome P450
- DEGs
- Differentially expressed genes
- EGS
- Eigengene score
- ER
- Endoplasmic reticulum
- FC
- Fold change
- KE
- Key event
- MIE
- Molecular initiating event
- MoA
- Mechanism of action
- MoS
- Margin of safety
- NAMs
- New approach methodologies
- NAPQI
- N-acetyl-p-benzoquinone imine
- NGRA
- Next generation risk assessment
- NPT
- 2,7-naphthalenediol
- NRF2
- Nuclear factor erythroid 2–related factor 2
- ORA
- Overrepresentation analysis
- PHH
- Primary human hepatocytes [plural noun]
- TCS
- Triclosan
- tPOD
- Transcriptomic point of departure
- TXG
- Toxicogenomics
- UPR
- Unfolded protein response
- VPA
- Valproic acid
- WTT
- William’s Trend Test