Abstract
Transcriptomics is developing into an invaluable tool in toxicology. The aim of this study was, using a transcriptomics approach, to identify genes that respond similar to many different chemicals (including drugs and industrial compounds) in both rat liver in vivo and in cultivated hepatocytes. For this purpose, we analyzed Affymetrix microarray expression data from 162 compounds that were previously tested in a concentration-dependent manner in rat livers in vivo and in rat hepatocytes cultivated in sandwich culture. These data were obtained from the Japanese Toxicogenomics Project (TGP) and North Rhine-Westphalian (NRW) data sets, which represent 138 and 29 compounds, respectively, and have only 5 compounds in common between them. The in vitro gene expression data from the NRW data set were generated in the present study, while TGP is publicly available. For each of the data sets, the overlap between up- or down-regulated genes in vitro and in vivo was identified, and named in vitro–in vivo consensus genes. Interestingly, the in vivo–in vitro consensus genes overlapped to a remarkable extent between both data sets, and were 21-times (upregulated genes) or 12-times (down-regulated genes) enriched compared to random expectation. Finally, the genes in the TGP and NRW overlap were used to identify the upregulated genes with the highest compound coverage, resulting in a seven-gene set of Cyp1a1, Ugt2b1, Cdkn1a, Mdm2, Aldh1a1, Cyp4a3, and Ehhadh. This seven-gene set was then successfully tested with structural analogues of valproic acid that are not present in the TGP and NRW data sets. In conclusion, the seven-gene set identified in the present study responds similarly in vitro and in vivo to a wide range of different chemicals. Despite these promising results with the seven-gene set, transcriptomics with cultivated rat hepatocytes remains a challenge, because in general many genes are up- or downregulated by in vitro culture per se, respond differently to test compounds in vitro and in vivo, and/or show higher variability in the in vitro system compared to the corresponding in vivo data.
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Acknowledgements
The read-across and bioinformatic parts of the study were supported by the European Union funded project EUToxRisk (EU, no. 681002). Additional parts were supported by the projects StemCellNet (BMBF, 01EK1604A), Liver Simulator (BMBF, 031A355A), DILI (BMBF, 031L0074F), LiSyM (BMBF, 031Loo45), LivSysTransfer (BMBF, 031L0119), DEEP (BMBF, 01KU1216) and test compounds of InnoSysTox (BMBF/EU, 031L0021A) were used.
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204_2018_2352_MOESM1_ESM.xlsx
Table S1: NRW data set of in vivo experiments. Matrix of the compounds for which gene array data of rat livers were generated (from: Ellinger-Ziegelbauer et al. 2008). Full and abbreviated compound names, doses in mg/(kg day), numbers of rats available for each exposure period, experimental series (as a possible source of batch effects), and the class of compounds, differentiating between genotoxic carcinogens, non-genotoxic carcinogens and non-hepatocarcinogens. NA: no information available. 6h, 12h, 48h, 1day, 3days, 7days, 14 days (XLSX 13 KB)
204_2018_2352_MOESM2_ESM.xlsx
Table S2: NRW data set of in vitro experiments. Matrix of the compounds for which gene array data of in vitro exposed cultivated rat hepatocytes were generated in the present study. Full and abbreviated compound names, the three concentrations in µM, number of samples obtained from different rats for each concentration, and the class of the compounds are given. Incubation of the cultivated rat hepatocytes with the test compounds was performed for 24h. NA: not available (XLSX 11 KB)
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Table S3: Matrix of the compounds tested in rats in vivo (TGP data set). The table gives full and abbreviated compound names as well as the dose in mg test compound per kg body weight and the number of independent replicates (no. of rats). Liver tissue was processed 24h after administration of the test compounds. For each liver one gene array data set was available (XLSX 14 KB)
204_2018_2352_MOESM4_ESM.xlsx
Table S4: Matrix of the compounds tested in cultivated primary rat hepatocytes (TGP). The table gives full and abbreviated compound names, the tested concentrations (low, middle and high concentration) in µM, and the number of independent replicates. For each replicate one gene array data set was available. The incubation period with test compounds was 24h. NA: no information available (XLSX 18 KB)
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Table S5: Concensus SV3 genes of the NRW database (369 up- and 326 down-regulated genes). The listed genes were up- or down-regulated in vivo (CSV 5 KB)
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Table S6: Concensus SV3 genes of the NRW database (354 up- and 1072 down-regulated genes). The listed genes were up- or down-regulated in vitro (CSV 10 KB)
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Table S7: Concensus SV3 genes of the TGP database (513 up- and 414 down-regulated genes). The listed genes were up- or down-regulated in vivo (CSV 6 KB)
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Table S8: Concensus SV3 genes of the TGP database (574 up- and 1228 down-regulated genes). The listed genes were up- or down-regulated in vitro (CSV 13 KB)
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Table S9: Concensus SV3 genes of the NRW database (77 up- and 98 down-regulated genes). The listed genes were up- or down-regulated both, in vitro and in vivo. The columns inducate the conditions under which up- or downregulation was observed (XLSX 32 KB)
204_2018_2352_MOESM10_ESM.xlsx
Table S10: Concensus SV3 genes of the TGP database (140 up- and 186 down-regulated genes). The listed genes were up- or down-regulated both, in vitro and in vivo. The columns inducate the conditions under which up- or downregulation was observed (XLSX 72 KB)
204_2018_2352_MOESM11_ESM.xlsx
Table S11: Listing of the 23 (22) TGP consensus SV3 genes out of 77 (98) NRW consensus SV3 genes which are up(down)-regulated in both in vitro and in vivo. The columns inducate the conditions under which up- or downregulation was observed. Table S11: Listing of the 23 (22) TGP consensus SV3 genes out of 77 (98) NRW consensus SV3 genes which are up(down)-regulated in both in vitro and in vivo. The columns inducate the conditions under which up- or downregulation was observed. Table S11: Listing of the 23 (22) TGP consensus SV3 genes out of 77 (98) NRW consensus SV3 genes which are up(down)-regulated in both in vitro and in vivo. The columns inducate the conditions under which up- or downregulation was observed (XLSX 27 KB)
204_2018_2352_MOESM12_ESM.xlsx
Table S12: Top-ranking genes of the combined (NRW and TGD) databases and their coverage. The table lists the compounds that are covered by each top-ranking gene. Coverage means that the indicated gene is up- or down-regulated by the covered compound (XLSX 12 KB)
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Table S13: Categorization as low- or high profile compound. Combined compound overview. The table gives full and abbreviated compound names, the data base from which they were obtained (NRW, TGP, or both) and the category (XLSX 16 KB)
204_2018_2352_MOESM14_ESM.xlsx
Table S14: Raw data of the validation experiments with 9 test compounds. The test compound, tested concentrations, and fold-change of each individual gene compared to solvent controls are given. Gene expression was determined by qRT-PCR. Cultivated hepatocytes of thee different rats (rat 1–3) were tested. Mean: mean value; SD: standard deviation (XLSX 50 KB)
204_2018_2352_MOESM15_ESM.pptx
Suppl. Fig. S1: Principle component analysis of gene expression data obtained from cultivated primary rat hepatocytes (in vitro) after incubation with 29 chemicals. The PCA plot is based on the 100 probe sets with highest variance and was generated to display the transcriptome data structure across replicates. Each point represents one experiment (data from one microarray). Cultivated hepatocytes were harvested at the beginning of the exposure period and after 24h exposure to test compounds and solvent. Suppl. Fig. S2: Principle component analysis of gene expression data obtained from rat liver samples (in vivo) after incubation with 30 (in vivo) chemicals. The PCA plot is based on the 100 probe sets with highest variance and was generated to display the transcriptome data structure across replicates. Each point represents one experiment (data from one microarray). Rat livers were analyzed 24h after administration of test compounds or solvent (controls) (PPTX 926 KB)
204_2018_2352_MOESM16_ESM.pptx
Suppl. Fig. S3: Principle component analysis of gene expression data obtained from rat liver (in vivo) and primary rat hepatocytes (in vitro) downloaded from the TG GATEs database (TGP). The cultivated rat hepatocytes were tested in duplicate with several using a low, middle and high concentration for different incubation periods (2h, 8h, 24h). Rat liver was tested in triplicate with three doses and sacrificed at 3h, 6h, 24h, 4 day, 8 day, 15 day and 29 day after exposure. For the present study only the in vitro data of the low (145 compounds), middle (140 compounds) and high concentration (138 compounds) and the 24h incubation period were used, as well as the in vivo data of the high dose and 24h incubation period (155 compounds). A. Overview of all samples and replicates. The dark and light green symbols illustrate the controls and exposed samples, respectively. B. Connecting lines between replicates illustrate the degree of variability. C. Mean values of the replicates. D. Connecting lines between controls (dark green) and corresponding compound exposed (light green) samples. E. Subtraction of the controls from the corresponding compound exposed samples. Suppl. Fig. S4: Number of significantly up- and down-regulated genes in the TGP data set. A. In vitro (green colored bar plots); B. In vivo (orange colored bar plots). The x-axis lists all tested chemicals. The y-axis gives the number of up- or down-regulated genes with at least 1.5-, 2.0-, and 3.0-fold changes (in vitro only fold change was calculated; in vivo fold change was calculated with adj.p.BH ≤ 0.01). Suppl. Fig. S5: Exclusivity analysis of the up- and down-regulated genes in TGP. This analysis first determines the 100 strongest upregulated genes across all compounds. Next, each of these genes is assigned to the compound with the most extreme fold change (‘match-winner’). A. In vitro. The dark green colored bars indicate the ‘match-winning’ events for each compound. The light green colored bars indicate, whether the top ranking genes meet our criteria ((> |3|-fold); B. In vivo. The dark orange colored bars indicate the ‘match-winning’ events for each compound; light orange colored bars indicate, whether the top ranking genes meet our criteria (> |3|-fold & adj.p.BH ≤ 0.01). Suppl. Fig. S6: A. Selection values of differentially expressed genes observed in the in vitro experiments of the TGP data set (> |3| fold); Since in vitro only two replicates are available per compound, genes were considered as up- or down-regulated when the mean difference to controls (fold change) was at least 3-fold (no p-value was calculated). A selection value of, e.g., three means that at least three compounds up- or downregulate the indicated gene. B. Selection values of differentially expressed genes in the in vivo experiments of the TGP data set (> |3|-fold & adj.p.BH ≤ 0.01). Since three biological replicates are available per compound, genes were considered as up-or downregulated when the mean difference to controls (fold change) was at least 3-fold and the adjusted p-value (Benjamini-Hochberg) was below 0.01; The left column shows the upregulated and the right column the downregulated genes (PPTX 1329 KB)
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Grinberg, M., Stöber, R.M., Albrecht, W. et al. Toxicogenomics directory of rat hepatotoxicants in vivo and in cultivated hepatocytes. Arch Toxicol 92, 3517–3533 (2018). https://doi.org/10.1007/s00204-018-2352-3
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DOI: https://doi.org/10.1007/s00204-018-2352-3