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Article ; Online: Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment.

Kunnen, Steven J / Arnesdotter, Emma / Willenbockel, Christian Tobias / Vinken, Mathieu / van de Water, Bob

2024 Volume 41, Issue 2, Page(s) 213–232

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 ... ...

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) 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.
MeSH term(s)	Animals ; Humans ; Gene Regulatory Networks ; Chemical Safety ; Hepatocytes ; Liver ; Gene Expression Profiling
Language	English
Publishing date	2024-02-20
Publishing country	Germany
Document type	Journal Article
ZDB-ID	165707-0
ISSN	1868-8551 ; 1018-4562 ; 0946-7785
ISSN (online)	1868-8551
ISSN	1018-4562 ; 0946-7785
DOI	10.14573/altex.2309201
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Article ; Online: Identifying multiscale translational safety biomarkers using a network-based systems approach.

Callegaro, Giulia / Schimming, Johannes P / Piñero González, Janet / Kunnen, Steven J / Wijaya, Lukas / Trairatphisan, Panuwat / van den Berk, Linda / Beetsma, Kim / Furlong, Laura I / Sutherland, Jeffrey J / Mollon, Jennifer / Stevens, James L / van de Water, Bob

iScience

2023 Volume 26, Issue 3, Page(s) 106094

Abstract: Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. ... ...

Abstract	Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human
Language	English
Publishing date	2023-01-31
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2023.106094
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Article: Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid.

Vrijenhoek, Nanette G / Wehr, Matthias M / Kunnen, Steven J / Wijaya, Lukas S / Callegaro, Giulia / Moné, Martijn J / Escher, Sylvia E / Van de Water, Bob

ALTEX

2022 Volume 39, Issue 2, Page(s) 207–220

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 ... ...

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.
MeSH term(s)	Carboxylic Acids/metabolism ; Hepatocytes/metabolism ; Liver ; Transcriptome ; Valproic Acid/metabolism ; Valproic Acid/toxicity
Chemical Substances	Carboxylic Acids ; Valproic Acid (614OI1Z5WI)
Language	English
Publishing date	2022-01-17
Publishing country	Germany
Document type	Journal Article
ZDB-ID	165707-0
ISSN	1868-596X ; 1018-4562 ; 0946-7785
ISSN	1868-596X ; 1018-4562 ; 0946-7785
DOI	10.14573/altex.2107261
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Article ; Online: A systems approach reveals species differences in hepatic stress response capacity.

Toxicological sciences : an official journal of the Society of Toxicology

2023 Volume 196, Issue 1, Page(s) 112–125

Abstract: To minimize the occurrence of unexpected toxicities in early phase preclinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical species and humans. Species differences in sensitivity to ... ...

Abstract	To minimize the occurrence of unexpected toxicities in early phase preclinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical species and humans. Species differences in sensitivity to acetaminophen (APAP) liver injury have been related to differences in the fraction of the drug that is bioactivated to the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI). We have used physiologically based pharmacokinetic modeling to identify oral doses of APAP (300 and 1000 mg/kg in mice and rats, respectively) yielding similar hepatic burdens of NAPQI to enable the comparison of temporal liver tissue responses under conditions of equivalent chemical insult. Despite pharmacokinetic and biochemical verification of the equivalent NAPQI insult, serum biomarker and tissue histopathology analyses revealed that mice still exhibited a greater degree of liver injury than rats. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats, indicative of a more robust transcriptional adaptation to the equivalent insult. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity. Multiomics analysis indicated that rats possess a greater basal and adaptive capacity for hepatic stress responses than mice and humans, with important implications for species selection and human translation in the safety testing of new drug candidates associated with reactive metabolite formation.
MeSH term(s)	Rats ; Mice ; Humans ; Animals ; Acetaminophen/toxicity ; Acetaminophen/metabolism ; Proteomics ; Species Specificity ; Chemical and Drug Induced Liver Injury/metabolism ; Liver/metabolism ; Oxidative Stress ; Systems Analysis
Chemical Substances	N-acetyl-4-benzoquinoneimine (G6S9BN13TI) ; Acetaminophen (362O9ITL9D)
Language	English
Publishing date	2023-08-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	1420885-4
ISSN	1096-0929 ; 1096-6080
ISSN (online)	1096-0929
ISSN	1096-6080
DOI	10.1093/toxsci/kfad085
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Article ; Online: An ensemble learning approach for modeling the systems biology of drug-induced injury.

Aguirre-Plans, Joaquim / Piñero, Janet / Souza, Terezinha / Callegaro, Giulia / Kunnen, Steven J / Sanz, Ferran / Fernandez-Fuentes, Narcis / Furlong, Laura I / Guney, Emre / Oliva, Baldo

Biology direct

2021 Volume 16, Issue 1, Page(s) 5

Abstract: Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one ...

Abstract	Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.
MeSH term(s)	Chemical and Drug Induced Liver Injury ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Machine Learning ; Models, Biological ; Pharmaceutical Preparations/chemistry ; Systems Biology
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2021-01-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2221028-3
ISSN	1745-6150 ; 1745-6150
ISSN (online)	1745-6150
ISSN	1745-6150
DOI	10.1186/s13062-020-00288-x
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Article ; Online: Comprehensive transcriptome analysis of fluid shear stress altered gene expression in renal epithelial cells.

Kunnen, Steven J / Malas, Tareq B / Semeins, Cornelis M / Bakker, Astrid D / Peters, Dorien J M

Journal of cellular physiology

2017 Volume 233, Issue 4, Page(s) 3615–3628

Abstract: Renal epithelial cells are exposed to mechanical forces due to flow-induced shear stress within the nephrons. Shear stress is altered in renal diseases caused by tubular dilation, obstruction, and hyperfiltration, which occur to compensate for lost ... ...

Abstract	Renal epithelial cells are exposed to mechanical forces due to flow-induced shear stress within the nephrons. Shear stress is altered in renal diseases caused by tubular dilation, obstruction, and hyperfiltration, which occur to compensate for lost nephrons. Fundamental in regulation of shear stress are primary cilia and other mechano-sensors, and defects in cilia formation and function have profound effects on development and physiology of kidneys and other organs. We applied RNA sequencing to get a comprehensive overview of fluid-shear regulated genes and pathways in renal epithelial cells. Functional enrichment-analysis revealed TGF-β, MAPK, and Wnt signaling as core signaling pathways up-regulated by shear. Inhibitors of TGF-β and MAPK/ERK signaling modulate a wide range of mechanosensitive genes, identifying these pathways as master regulators of shear-induced gene expression. However, the main down-regulated pathway, that is, JAK/STAT, is independent of TGF-β and MAPK/ERK. Other up-regulated cytokine pathways include FGF, HB-EGF, PDGF, and CXC. Cellular responses to shear are modified at several levels, indicated by altered expression of genes involved in cell-matrix, cytoskeleton, and glycocalyx remodeling, as well as glycolysis and cholesterol metabolism. Cilia ablation abolished shear induced expression of a subset of genes, but genes involved in TGF-β, MAPK, and Wnt signaling were hardly affected, suggesting that other mechano-sensors play a prominent role in the shear stress response of renal epithelial cells. Modulations in signaling due to variations in fluid shear stress are relevant for renal physiology and pathology, as suggested by elevated gene expression at pathological levels of shear stress compared to physiological shear.
MeSH term(s)	Animals ; Cells, Cultured ; Down-Regulation/physiology ; Epithelial Cells/metabolism ; Gene Expression/physiology ; Gene Expression Profiling/methods ; Kidney/metabolism ; Mice, Transgenic ; Signal Transduction/physiology ; Stress, Mechanical ; Up-Regulation
Language	English
Publishing date	2017-11-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	3116-1
ISSN	1097-4652 ; 0021-9541
ISSN (online)	1097-4652
ISSN	0021-9541
DOI	10.1002/jcp.26222
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Article ; Online: Comparative transcriptomics of shear stress treated Pkd1

Kunnen, Steven J / Malas, Tareq B / Formica, Chiara / Leonhard, Wouter N / 't Hoen, Peter A C / Peters, Dorien J M

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

2018 Volume 108, Page(s) 1123–1134

Abstract: Mutations in the PKD1 or PKD2 genes are the cause of autosomal dominant polycystic kidney disease (ADPKD). The encoded proteins localize within the cell membrane and primary cilia and are proposed to be involved in mechanotransduction. Therefore, we ... ...

Abstract	Mutations in the PKD1 or PKD2 genes are the cause of autosomal dominant polycystic kidney disease (ADPKD). The encoded proteins localize within the cell membrane and primary cilia and are proposed to be involved in mechanotransduction. Therefore, we evaluate shear stress dependent signaling in renal epithelial cells and the relevance for ADPKD. Using RNA sequencing and pathway analysis, we compared gene expression of in vitro shear stress treated Pkd1
MeSH term(s)	Animals ; Cilia/metabolism ; Epithelial Cells/metabolism ; Gene Deletion ; Gene Expression Profiling ; Kidney Tubules, Proximal/pathology ; Male ; Mice ; Organ Size ; Polycystic Kidney Diseases/genetics ; Polycystic Kidney Diseases/pathology ; Signal Transduction/genetics ; Stress, Mechanical ; TRPP Cation Channels/deficiency ; TRPP Cation Channels/metabolism ; Transcription, Genetic
Chemical Substances	TRPP Cation Channels ; polycystic kidney disease 1 protein
Language	English
Publishing date	2018-10-01
Publishing country	France
Document type	Comparative Study ; Journal Article
ZDB-ID	392415-4
ISSN	1950-6007 ; 0753-3322 ; 0300-0893
ISSN (online)	1950-6007
ISSN	0753-3322 ; 0300-0893
DOI	10.1016/j.biopha.2018.07.178
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Article ; Online: The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Callegaro, Giulia / Kunnen, Steven J / Trairatphisan, Panuwat / Grosdidier, Solène / Niemeijer, Marije / den Hollander, Wouter / Guney, Emre / Piñero Gonzalez, Janet / Furlong, Laura / Webster, Yue W / Saez-Rodriguez, Julio / Sutherland, Jeffrey J / Mollon, Jennifer / Stevens, James L / van de Water, Bob

Archives of toxicology

2021 Volume 95, Issue 12, Page(s) 3745–3775

Abstract: Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative ... ...

Abstract	Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.
MeSH term(s)	Acetaminophen/toxicity ; Animals ; Chemical and Drug Induced Liver Injury/etiology ; Chemical and Drug Induced Liver Injury/genetics ; Cyclosporine/toxicity ; Datasets as Topic ; Endoplasmic Reticulum Stress/drug effects ; Gene Expression Profiling ; Gene Regulatory Networks ; Hepatocytes/drug effects ; Hepatocytes/pathology ; Humans ; Oxidative Stress/drug effects ; Rats ; Risk Assessment/methods ; Species Specificity ; Toxicogenetics/methods ; Tunicamycin/toxicity
Chemical Substances	Tunicamycin (11089-65-9) ; Acetaminophen (362O9ITL9D) ; Cyclosporine (83HN0GTJ6D)
Language	English
Publishing date	2021-10-09
Publishing country	Germany
Document type	Journal Article
ZDB-ID	124992-7
ISSN	1432-0738 ; 0340-5761
ISSN (online)	1432-0738
ISSN	0340-5761
DOI	10.1007/s00204-021-03141-w
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Article ; Online: An ensemble learning approach for modeling the systems biology of drug-induced injury

Joaquim Aguirre-Plans / Janet Piñero / Terezinha Souza / Giulia Callegaro / Steven J. Kunnen / Ferran Sanz / Narcis Fernandez-Fuentes / Laura I. Furlong / Emre Guney / Baldo Oliva

Biology Direct, Vol 16, Iss 1, Pp 1-

2021 Volume 14

Abstract: Abstract Background Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite ... ...

Abstract	Abstract Background Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.
Keywords	CAMDA ; Drug-induced liver injury ; Hepatotoxicity ; Drug safety ; Systems biology ; Machine learning ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
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Article ; Online: High-Throughput Phenotypic Screening of Kinase Inhibitors to Identify Drug Targets for Polycystic Kidney Disease.

Booij, Tijmen H / Bange, Hester / Leonhard, Wouter N / Yan, Kuan / Fokkelman, Michiel / Kunnen, Steven J / Dauwerse, Johannes G / Qin, Yu / van de Water, Bob / van Westen, Gerard J P / Peters, Dorien J M / Price, Leo S

SLAS discovery : advancing life sciences R & D

2017 Volume 22, Issue 8, Page(s) 974–984

Abstract: Polycystic kidney disease (PKD) is a prevalent disorder characterized by renal cysts that lead to kidney failure. Various signaling pathways have been targeted to stop disease progression, but most interventions still focus on alleviating PKD-associated ... ...

Abstract	Polycystic kidney disease (PKD) is a prevalent disorder characterized by renal cysts that lead to kidney failure. Various signaling pathways have been targeted to stop disease progression, but most interventions still focus on alleviating PKD-associated symptoms. The mechanistic complexity of the disease, as well as the lack of functional in vitro assays for compound testing, has made drug discovery for PKD challenging. To identify modulators of PKD, Pkd1
MeSH term(s)	Animals ; Cell Line ; Colforsin ; High-Throughput Screening Assays/methods ; Hydrogel, Polyethylene Glycol Dimethacrylate ; Kidney Tubules, Collecting/drug effects ; Kidney Tubules, Collecting/pathology ; Mice ; Molecular Targeted Therapy ; Phenotype ; Phosphatidylinositol 3-Kinases/metabolism ; Phosphoinositide-3 Kinase Inhibitors ; Polycystic Kidney Diseases/drug therapy ; Polycystic Kidney Diseases/pathology ; Protein Kinase Inhibitors/analysis ; Protein Kinase Inhibitors/pharmacology ; Protein Kinase Inhibitors/therapeutic use ; TOR Serine-Threonine Kinases/antagonists & inhibitors ; TOR Serine-Threonine Kinases/metabolism
Chemical Substances	Phosphoinositide-3 Kinase Inhibitors ; Protein Kinase Inhibitors ; Colforsin (1F7A44V6OU) ; Hydrogel, Polyethylene Glycol Dimethacrylate (25852-47-5) ; TOR Serine-Threonine Kinases (EC 2.7.11.1)
Language	English
Publishing date	2017-06-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2885123-7
ISSN	2472-5560 ; 2472-5552
ISSN (online)	2472-5560
ISSN	2472-5552
DOI	10.1177/2472555217716056
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