Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid
Article Sidebar
Main Article Content
Abstract
Chemical read-across is commonly evaluated without specific knowledge of the biological mechanisms leading to observed adverse outcomes in vivo. Integrating data that indicate shared modes of action in humans will strengthen read-across cases. Here we studied transcriptomic responses of primary human hepatocytes (PHH) to a large panel of carboxylic acids to include detailed mode-of-action data as a proof-of-concept for read-across in risk assessment. In rodents, some carboxylic acids, including valproic acid (VPA), are known to cause hepatic steatosis, whereas others do not. We investigated transcriptomics responses of PHHs exposed for 24 h to 18 structurally different VPA analogues in a concentration range to determine biological similarity in relation to in vivo steatotic potential. Using a targeted high-throughput screening assay, we assessed the differential expression of ~3,000 genes covering relevant biological pathways. Differentially expressed gene analysis revealed differences in potency of carboxylic acids, and expression patterns were highly similar for structurally similar compounds. Strong clustering occurred for steatosis-positive versus steatosis-negative carboxylic acids. To quantitatively define biological read-across, we combined pathway analysis and weighted gene co-expression network analysis. Active carboxylic acids displayed high similarity in gene network modulation. Importantly, free fatty acid synthesis modulation and stress pathway responses are affected by active carboxylic acids, providing coherent mechanistic underpinning for our findings. Our work shows that transcriptomic analysis of cultured human hepatocytes can reinforce the prediction of liver injury outcome based on quantitative and mechanistic biological data and support its application in read-across.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited (CC-BY). Copyright on any article in ALTEX is retained by the author(s).
Abdel-Dayem, M. A., Elmarakby, A. A., Abdel-Aziz, A. A. et al. (2014). Valproate-induced liver injury: Modulation by the omega-3 fatty acid DHA proposes a novel anticonvulsant regimen. Drugs R D 14, 85-94. doi:10.1007/s40268-014-0042-z
Acosta Jr, D. (2012). Pharmaceuticals. In E. Bingham and B. Cohrssen (eds.), Patty’s Toxicology (583-602). John Wiley & Sons, Inc. doi:10.1002/9780471125471.tox001.pub2
Baillie, T. A. (1992). Metabolism of valproate to hepatotoxic intermediates. Pharm Weekbl Sci Ed 14, 122-125. doi:10.1007/BF01962701
Bourin, M. (2020). Mechanism of action of valproic acid and its derivatives. SOJ Pharm Pharm Sci 7, 1-4. doi:10.15226/2374-6866/7/1/00199
Callegaro, G., Kunnen, S. J., Trairatphisan, P. et al. (2021). The human hepatocyte TXG-MAPr: Gene co-expression network modules to support mechanism-based risk assessment. Arch Toxicol 95, 3745-3775. doi:10.1007/s00204-021-03141-w
Escher, S. E., Kamp, H., Bennekou, S. H. et al. (2019). Towards grouping concepts based on new approach methodologies in chemical hazard assessment: The read-across approach of the EU-ToxRisk project. Arch Toxicol 93, 3643-3667. doi:10.1007/s00204-019-02591-7
Escher, S. E., Aguayo-Orozco, A., Benfenati, E. et al. (2021). A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action. Toxicol In Vitro 79, 105269. doi:10.1016/j.tiv.2021.105269
Espandiari, P., Zhang, J., Schnackenberg, L. K. et al. (2008). Age-related differences in susceptibility to toxic effects of valproic acid in rats. J Appl Toxicol 28, 628-637. doi:10.1002/jat.1314
Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M. et al. (2019). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res 29, 1363-1375. doi:10.1101/gr.240663.118
Gwinn, W. M., Auerbach, S. S., Parham, F. et al. (2020). Evaluation of 5-day in vivo rat liver and kidney with high-throughput transcriptomics for estimating benchmark doses of apical outcomes. Toxicol Sci 176, 343-354. doi:10.1093/toxsci/kfaa081
Harrill, J. A., Everett, L. J., Haggard, D. E. et al. (2021). High-throughput transcriptomics platform for screening environmental chemicals. Toxicol Sci 181, 68-89. doi:10.1093/toxsci/kfab009
He, J., Zhang, P., Shen, L. et al. (2020). Short-chain fatty acids and their association with signalling pathways in inflammation, glucose and lipid metabolism. Int J Mol Sci 21, 6356. doi:10.3390/ijms21176356
Huppelschoten, S. (2017). Dynamics of TNFalpha Signaling and Drug-Related Liver Toxicity. Leiden University.
Ibrahim, M. A. (2012). Evaluation of hepatotoxicity of valproic acid in albino mice, histological and histoistochemical studies. Life Sci J 9, 153-159. https://www.researchgate.net/publication/285774185
Juberg, D. R., David, R. M., Katz, G. V. et al. (1998). 2-ethylhexanoic acid: Subchronic oral toxicity studies in the rat and mouse. Food Chem Toxicol 36, 429-436. doi:10.1016/S0278-6915(97)00168-3
Kavlock, R. J., Bahadori, T., Barton-Maclaren, T. S. et al. (2018). Accelerating the pace of chemical risk assessment. Chem Res Toxicol 31, 287-290. doi:10.1021/acs.chemrestox.7b00339
Knapp, A. C., Todesco, L., Beier, K. et al. (2008). Toxicity of valproic acid in mice with decreased plasma and tissue carnitine stores. J Pharmacol Exp Ther 324, 568-575. doi:10.1124/jpet.107.131185
Knottnerus, S. J. G., Bleeker, J. C., Wüst, R. C. I. et al. (2018). Disorders of mitochondrial long-chain fatty acid oxidation and the carnitine shuttle. Rev Endocr Metab Disord 19, 93-106. doi:10.1007/s11154-018-9448-1
Landesmann, B., Gomenou, M., Munn, S. et al. (2012). Description of Prototype Modes-of-Action Related to Repeated Dose Toxicity. EUR 25631 EN. Luxembourg (Luxembourg): Publications Office of the European Union. JRC75689. doi:10.2788/71112
Limonciel, A., Ates, G., Carta, G. et al. (2018). Comparison of base-line and chemical-induced transcriptomic responses in HepaRG and RPTEC/TERT1 cells using TempO-Seq. Arch Toxicol 92, 2517-2531. doi:10.1007/s00204-018-2256-2
Löscher, W., Wahnschaffe, U., Hönack, D. et al. (1992). Effects of valproate and E-2-en-valproate on functional and morphological parameters of rat liver. I. Biochemical, histopathological and pharmacokinetic studies. Epilepsy Res 13, 187-198. doi:10.1016/0920-1211(92)90052-U
Love, M. I., Huber, W. and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. doi:10.1186/s13059-014-0550-8
Mav, D., Shah, R. R., Howard, B. E. et al. (2018). A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS One 13, e0191105. doi:10.1371/journal.pone.0191105
Patlewicz, G., Kuseva, C., Kesova, A. et al. (2014). Towards AOP application – Implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization. Regul Toxicol Pharmacol 69, 529-545. doi:10.1016/j.yrtph.2014.06.001
Pistollato, F., Madia, F., Corvi, R. et al. (2021). Current EU regulatory requirements for the assessment of chemicals and cosmetic products: Challenges and opportunities for introducing new approach methodologies. Arch Toxicol 95, 1867-1897. doi:10.1007/s00204-021-03034-y
Ramaiahgari, S. C., Auerbach, S. S., Saddler, T. O. et al. (2019). The power of resolution: Contextualized understanding of biological responses to liver injury chemicals using high-throughput transcriptomics and benchmark concentration modeling. Toxicol Sci 169, 553-566. doi:10.1093/toxsci/kfz065
Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140. doi:10.1093/bioinformatics/btp616
Rovida, C., Escher, S. E., Herzler, M. et al. (2021). NAM-supported read-across: From case studies to regulatory guidance in safety assessment. ALTEX 38, 140-150. doi:10.14573/altex.2010062
Schultz, T. W., Amcoff, P., Berggren, E. et al. (2015). A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol 72, 586-601. doi:10.1016/j.yrtph.2015.05.016
Semino, G. (1998). Saturated aliphatic acyclic branched-chain primary alcohols, aldehydes, and acids. WHO, Geneva. http://www.inchem.org/documents/jecfa/jecmono/v040je11.htm
Serra, A., Fratello, M., Cattelani, L. et al. (2020). Transcriptomics in toxicogenomics, part III: Data modelling for risk assessment. Nanomaterials 10, 708. doi:10.3390/nano10040708
Silva, M. F. B., Aires, C. C. P., Luis, P. B. M. et al. (2008). Valproic acid metabolism and its effects on mitochondrial fatty acid oxidation: A review. J Inherit Metab Dis 31, 205-216. doi:10.1007/s10545-008-0841-x
Sugimoto, T., Woo, M., Nishida, N. et al. (1987). Hepatotoxicity in rat following administration of valproic acid. Epilepsia 28, 142-146. doi:10.1111/j.1528-1157.1987.tb03640.x
Szalowska, E., Van Der Burg, B., Man, H. Y. et al. (2014). Model steatogenic compounds (amiodarone, valproic acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices. PLoS One 9, e86795. doi:10.1371/journal.pone.0086795
Szilagyi, M. (2012). Aliphatic carboxylic acids: Saturated. In E. Bingham and B. Cohrssen (eds.), Patty’s Toxicology (471-532). John Wiley & Sons, Inc. doi:10.1002/9780471125471.tox001.pub2
Tang, W., Borel, A. G., Abbott, F. S. et al. (1995). Fluorinated analogues as mechanistic probes in valproic acid hepatotoxicity: Hepatic microvesicular steatosis and glutathione status. Chem Res Toxicol 8, 671-682. doi:10.1021/tx00047a006
Thayer, W. S. (1984). Inhibition of mitochondrial fatty acid oxidation in pentenoic acid-induced fatty liver. A possible model for Reye’s syndrome. Biochem Pharmacol 33, 1187-1194. doi:10.1016/0006-2952(84)90169-2
Tollefsen, K. E., Scholz, S., Cronin, M. T. et al. (2014). Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul Toxicol Pharmacol 70, 629-640. doi:10.1016/j.yrtph.2014.09.009
Tonelli, C., Chio, I. I. C. and Tuveson, D. A. (2018). Transcriptional regulation by Nrf2. Antioxid Redox Signal 29, 1727-1745. doi:10.1089/ars.2017.7342
Tong, V., Teng, X. W., Chang, T. K. H. et al. (2005). Valproic acid I: Time course of lipid peroxidation biomarkers, liver toxicity, and valproic acid metabolite levels in rats. Toxicol Sci 86, 427-435. doi:10.1093/toxsci/kfi184
van Breda, S. G. J., Claessen, S. M. H., van Herwijnen, M. et al. (2018). Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction. Toxicology 393, 160-170. doi:10.1016/j.tox.2017.11.013
Vinken, M. (2015). Adverse outcome pathways and drug-induced liver injury testing. Chem Res Toxicol 28, 1391-1397. doi:10.1021/acs.chemrestox.5b00208
Wickham, H. (2007). Reshaping data with the {reshape} package. J Stat Softw 21, 1-20. doi:10.18637/jss.v021.i12
Yang, H., Niemeijer, M., van de Water, B. et al. (2020). ATF6 is a critical determinant of CHOP dynamics during the unfolded protein response. iScience 23, 100860. doi:10.1016/j.isci.2020.100860
Yeakley, J. M., Shepard, P. J., Goyena, D. E. et al. (2017). A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLoS One 12, e0178302. doi:10.1371/journal.pone.0178302
Yuge, K. (1990). Carnitine metabolism in rats with 4-pentenoic acid induced fatty liver. Pediatr Int 32, 449-455. doi:10.1111/j.1442-200X.1990.tb00859.x