An ensemble learning approach for modeling the systems biology of drug-induced injury

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.The authors received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements TransQST and eTRANSAFE (refs: 116030, 777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The authors also received support from Spanish Ministry of Economy (MINECO, refs: BIO2017–85329-R (FEDER, EU), RYC-2015-17519) as well as EU H2020 Programme 2014–2020 under grant agreement No. 676559 (Elixir-Excelerate) and from Agència de Gestió D’ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). L.I.F. received support from ISCIII-FEDER (ref: CPII16/00026). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). J.A.P. received support from the CAMDA Travel Fellowship

Curcumin inhibits cystogenesis by simultaneous interference of multiple signaling pathways:in vivo evidence from a Pkd1-deletion model

Leonhard WN, van der Wal A, Novalic Z, Kunnen SJ, Gansevoort RT, Breuning MH, de Heer E, Peters DJ. Curcumin inhibits cystogenesis by simultaneous interference of multiple signaling pathways: in vivo evidence from a Pkd1-deletion model. Am J Physiol Renal Physiol 300: F1193-F1202, 2011. First published February 23, 2011; doi:10.1152/ajprenal.00419.2010.-Autosomal dominant polycystic kidney disease (ADPKD) caused by mutations in either the PKD1 or PKD2 gene is a major cause of end-stage renal failure. A number of compounds targeting specific signaling pathways were able to inhibit cystogenesis in rodent models and are currently being tested in clinical trials. However, given the complex signaling in ADPKD, an ideal therapy would likely have to comprise several pathways at once. Therefore, multitarget compounds may provide promising therapeutic interventions for the treatment of ADPKD. To test this hypothesis, we treated Pkd1-deletion mice with diferuloylmethane (curcumin), a compound without appreciable side effects and known to modulate several pathways that are also altered in ADPKD, e.g., mammalian target of rapamycin (mTOR) and Wnt signaling. After conditional inactivation of Pkd1, mTOR signaling was indeed elevated in cystic kidneys. Interestingly, also activation of signal transducers and activator of transcription 3 (STAT3) strongly correlated with cyst progression. Both pathways were effectively inhibited in vitro by curcumin. Importantly, Pkd1-deletion mice that were treated with curcumin and killed at an early stage of PKD displayed improved renal histology and reduced STAT3 activation, proliferation index, cystic index, and kidney weight/body weight ratios. In addition, renal failure was significantly postponed in mice with severe PKD. These data suggest that multitarget compounds hold promising potential for safe and effective treatment of ADPKD

High-Throughput Phenotypic Screening of Kinase Inhibitors to Identify Drug Targets for Polycystic Kidney Disease

Functional Genomics of Systemic Disorder

Identifying multiscale translational safety biomarkers using a network-based systems approach

Summary: Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

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

The human hepatocyte TXG-MAPr

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.</p

A systems approach reveals species differences in hepatic stress response capacity

ABSTRACTTo minimise unexpected toxicities in early phase clinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical test species and humans. We have used physiologically-based pharmacokinetic modelling to identify doses of the model hepatotoxin acetaminophen yielding similar hepatic burdens of the reactive metabolite N-acetyl-p-benzoquinoneimine in mice and rats, to enable comparison of tissue adaptive responses under conditions of equivalent chemical insult. Mice exhibited a greater degree of liver injury than rats, despite the equivalent hepatic NAPQI burden. 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. 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, and have important implications for species selection and human translation in the safety testing of new drug candidates.</jats:p

Fluid shear stress-induced TGF-β/ALK5 signaling in renal epithelial cells is modulated by MEK1/2

Renal tubular epithelial cells are exposed to mechanical forces due to fluid flow shear stress within the lumen of the nephron. These cells respond by activation of mechano-sensors located at the plasma membrane or the primary cilium, having crucial roles in maintenance of cellular homeostasis and signaling. In this paper, we applied fluid shear stress to study TGF-β signaling in renal epithelial cells with and without expression of the Pkd1-gene, encoding a mechano-sensor mutated in polycystic kidney disease. TGF-β signaling modulates cell proliferation, differentiation, apoptosis, and fibrotic deposition, cellular programs that are altered in renal cystic epithelia. SMAD2/3-mediated signaling was activated by fluid flow, both in wild-type and Pkd1−/− cells. This was characterized by phosphorylation and nuclear accumulation of p-SMAD2/3, as well as altered expression of downstream target genes and epithelial-to-mesenchymal transition markers. This response was still present after cilia ablation. An inhibitor of upstream type-I-receptors, ALK4/ALK5/ALK7, as well as TGF-β-neutralizing antibodies effectively blocked SMAD2/3 activity. In contrast, an activin-ligand trap was ineffective, indicating that increased autocrine TGF-β signaling is involved. To study potential involvement of MAPK/ERK signaling, cells were treated with a MEK1/2 inhibitor. Surprisingly, fluid flow-induced expression of most SMAD2/3 targets was further enhanced upon MEK inhibition. We conclude that fluid shear stress induces autocrine TGF-β/ALK5-induced target gene expression in renal epithelial cells, which is partially restrained by MEK1/2-mediated signaling.Multi Phase System