Integrating multiple omics to unravel mechanisms of Cyclosporin A induced hepatotoxicity in vitro

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

BACKGROUND

Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships.

METHODS

Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship.

RESULTS

This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis.

CONCLUSIONS

This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

Metabolomic Analysis of Pediatric Patients with Idiosyncratic Drug-Induced Liver Injury According to the Updated RUCAM

Hepatotoxicity, a common adverse drug effect, has been extensively studied in adult patients. However, it is equally important to investigate this condition in pediatric patients to develop personalized treatment strategies for children. This study aimed to identify plasma biomarkers that characterize hepatotoxicity in pediatric patients through an observational case-control study. Metabolomic analysis was conducted on 55 pediatric patients with xenobiotic liver toxicity and 88 healthy controls. The results revealed clear differences between the two groups. Several metabolites, including hydroxydecanoylcarnitine, octanoylcarnitine, lysophosphatidylcholine, glycocholic acid, and taurocholic acid, were identified as potential biomarkers (area under the curve: 0.817; 95% confidence interval: 0.696-0.913). Pathway analysis indicated involvement of primary bile acid biosynthesis and the metabolism of taurine and hypotaurine (p < 0.05). The findings from untargeted metabolomic analysis demonstrated an increase in bile acids in children with hepatotoxicity. The accumulation of cytotoxic bile acids should be further investigated to elucidate the role of these metabolites in drug-induced liver injury.

Intracellular and extracellular untargeted metabolomics reveal the effect of acute uranium exposure in HK-2 cells

Uranium exposure poses a serious threat to the health of occupational populations and the public. Although metabolomics is a promising research approach to study the toxicological mechanisms of uranium exposure, in vitro studies using human cells are scarce. Applying cultured cell metabolomics, we exhaustively analyzed the intracellular and extracellular differential metabolites upon uranium exposure and characterized the possible biological effects of uranium exposure on human kidney cells. Uranium exposure significantly induced disturbance in the amino acid biosynthesis and linoleic acid metabolism of the cells. Cells exposed to uranium produce excessive amounts of arachidonic acid, which has the potential to cause oxidative stress and damage cells. The results provide new evidence for an oxidative stress mechanism of uranium-induced renal cell injury. Cell metabolomics has proven to be a useful diagnostic tool to study the molecular mechanisms of uranium poisoning.

Identification of key genes and pathways between mild-moderate and severe asthmatics via bioinformatics analysis

Severe asthma is the main reason for death and disability caused by asthma. However, effective biomarkers for severe asthma have not been identified. Here, we aimed to identify potential biomarkers in severe asthma. We identified 202 differentially expressed genes (DEGs) between severe asthma and mild-moderate asthma after integrating the results from GSE69683 and GSE27011 datasets. The enrichment analysis indicated that 202 DEGs were associated with metabolism- and immune-related processes. 10 hub genes were identified by Cytoscape and five of these genes’ AUC (area under the curve) values were greater than 0.6 in GSE69683. The AUC value reached to 0.701 when combined SEC61A1 and ALDH18A1 expression. The expression of the five hub genes was verified in an external dataset. The network analysis revealed that transcription factor (TF) WT1, ZEB1, RERE, FOSL1, and miR-20a may be involved in the development of asthma. In addition, we found cyclosporine and acetaminophen could interact with these hub genes and may be negatively associated with most of the five hub genes according to previous reports. Overall, key genes were identified between mild-moderate and severe asthmatics, which contributed to the understanding of the development of asthma.

RNA modifications as emerging therapeutic targets

The field of epitranscriptome, posttranscriptional modifications to RNAs, is still growing up and has presented substantial evidences for the role of RNA modifications in diseases. In terms of new drug development, RNA modifications have a great promise for therapy. For example, more than 170 type of modifications exist in various types of RNAs. Regulatory genes and their roles in critical biological process have been identified and they are associated with several diseases. Current data, for example, identification of inhibitors targeting RNA modifications regulatory genes, strongly support the idea that RNA modifications have potential as emerging therapeutic targets. Therefore, in this review, RNA modifications and regulatory genes were comprehensively documented in terms of drug development by summarizing the findings from previous studies. It was discussed how RNA modifications or regulatory genes can be targeted by altering molecular mechanisms. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Processing > RNA Editing and Modification.

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.

Testing in vitro tools for the prediction of cholestatic liver injury induced by non-pharmaceutical chemicals

Bile acid accumulation and subsequent liver damage is a frequent adverse effect induced by drugs. Considerable efforts have therefore been focused on the introduction and characterization of tools that allow reliable prediction of this type of drug-induced liver injury. Among those are the cholestatic index and transcriptomic profiling, which are typically assessed in in vitro settings. The present study was set up to test the applicability of both tools to non-pharmaceutical compounds with cholestatic potential, including the industrial compound bis(2-ethylhexyl)phthalate, the cosmetic ingredients triclosan and octynoic acid, the herbicides paraquat and quizalofop-para-ethyl, and the food additives sunset yellow and tartrazine, in a human hepatoma cell culture model of cholestatic liver injury. The cholestatic index method showed cholestatic liability of sunset yellow, tartrazine and triclosan. Of those, tartrazine induced transcriptional changes reminiscent of the transcriptional profile of cholestatic drugs. Furthermore, a number of genes were found to be uniquely modulated by tartrazine, in accordance with the cholestatic drugs atazanavir, cyclosporin A and nefazodone, which may have potential as novel transcriptomic biomarkers of chemical-induced cholestatic liver injury. In conclusion, unambiguous identification of the non-pharmaceutical compounds tested in this study as inducers of cholestasis could not be achieved.

Repeated dose multi-drug testing using a microfluidic chip-based coculture of human liver and kidney proximal tubules equivalents

A microfluidic multi-organ chip emulates the tissue culture microenvironment, enables interconnection of organ equivalents and overcomes interspecies differences, making this technology a promising and powerful tool for preclinical drug screening. In this study, we established a microfluidic chip-based model that enabled non-contact cocultivation of liver spheroids and renal proximal tubule barriers in a connecting media circuit over 16 days. Meanwhile, a 14-day repeated-dose systemic administration of cyclosporine A (CsA) alone or in combination with rifampicin was performed. Toxicity profiles of the two different doses of CsA on different target organs could be discriminated and that concomitant treatment with rifampicin from day6 onwards decreased the CsA concentration and attenuated the toxicity compared with that after treatment with CsA for 14 consecutive days. The latter is manifested with the changes in cytotoxicity, cell viability and apoptosis, gene expression of metabolic enzymes and transporters, and noninvasive toxicity biomarkers. The on chip coculture of the liver and the proximal tubulus equivalents showed its potential as an effective and translational tool for repeated dose multi-drug toxicity screening in the preclinical stage of drug development.

Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury

Adverse outcome pathways (AOPs) have been recently introduced as tools to map the mechanisms underlying toxic events relevant for chemical risk assessment. AOPs particularly depict the linkage between a molecular initiating event and an adverse outcome through a number of intermediate key events. An AOP has been previously introduced for cholestatic liver injury. The objective of this study was to test the robustness of this AOP for different types of cholestatic insult and the in vitro to in vivo extrapolation. For this purpose, in vitro samples from human hepatoma HepaRG cell cultures were exposed to cholestatic drugs (i.e. intrahepatic cholestasis), while in vivo samples were obtained from livers of cholestatic mice (i.e. extrahepatic cholestasis). The occurrence of cholestasis in vitro was confirmed through analysis of bile transporter functionality and bile acid analysis. Transcriptomic analysis revealed inflammation and oxidative stress as key events in both types of cholestatic liver injury. Major transcriptional differences between intrahepatic and extrahepatic cholestatic liver insults were observed at the level of cell death and metabolism. Novel key events identified by pathway analysis included endoplasmic reticulum stress in intrahepatic cholestasis, and autophagy and necroptosis in both intrahepatic as extrahepatic cholestasis. This study demonstrates that AOPs constitute dynamic tools that should be frequently updated with new input information.

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Primary hepatocytes and their cultures for the testing of drug-induced liver injury

Drug-induced liver injury is a major reason for discontinuation of drug development and withdrawal of drugs from the market. Intensive efforts in the last decades have focused on the establishment and finetuning of liver-based in vitro models for reliable prediction of hepatotoxicity triggered by drug candidates. Of those, primary hepatocytes and their cultures still are considered the gold standard, as they provide an acceptable reflection of the hepatic in vivo situation. Nevertheless, these in vitro systems cope with gradual deterioration of the differentiated morphological and functional phenotype. The present paper gives an overview of traditional and more recently introduced strategies to counteract this dedifferentiation process in an attempt to set up culture models that can be used for long-term testing purposes. The relevance and applicability of such optimized cultures of primary hepatocytes for the testing of drug-induced cholestatic liver injury is demonstrated.

Untargeted Metabolomic Analysis of Rat Neuroblastoma Cells as a Model System to Study the Biochemical Effects of the Acute Administration of Methamphetamine

Methamphetamine is an illicit psychostimulant drug that is linked to a number of diseases of the nervous system. The downstream biochemical effects of its primary mechanisms are not well understood, and the objective of this study was to investigate whether untargeted metabolomic analysis of an in vitro model could generate data relevant to what is already known about this drug. Rat B50 neuroblastoma cells were treated with 1 mM methamphetamine for 48 h, and both intracellular and extracellular metabolites were profiled using gas chromatography–mass spectrometry. Principal component analysis of the data identified 35 metabolites that contributed most to the difference in metabolite profiles. Of these metabolites, the most notable changes were in amino acids, with significant increases observed in glutamate, aspartate and methionine, and decreases in phenylalanine and serine. The data demonstrated that glutamate release and, subsequently, excitotoxicity and oxidative stress were important in the response of the neuronal cell to methamphetamine. Following this, the cells appeared to engage amino acid-based mechanisms to reduce glutamate levels. The potential of untargeted metabolomic analysis has been highlighted, as it has generated biochemically relevant data and identified pathways significantly affected by methamphetamine. This combination of technologies has clear uses as a model for the study of neuronal toxicology.

An Update on Adverse Outcome Pathways Leading to Liver Injury

Chemical-induced liver injury can be manifested in a number of ways, such as cholestasis, steatosis, fibrosis, and cancer. The mechanisms driving these toxicological processes have been well characterized and have been embedded in adverse outcome pathway frameworks in recent years. This article provides a concise overview of these constructs.

Experimental design and reporting standards for metabolomics studies of mammalian cell lines

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics

Cholestasis is one of the major causes of drug-induced liver injury (DILI), which can result in withdrawal of approved drugs from the market. Early identification of cholestatic drugs is difficult due to the complex mechanisms involved. In order to develop a strategy for mechanism-based risk assessment of cholestatic drugs, we analyzed gene expression data obtained from the livers of rats that had been orally administered with 12 known cholestatic compounds repeatedly for 28 days at three dose levels. Qualitative analyses were performed using two statistical approaches (hierarchical clustering and principle component analysis), in addition to pathway analysis. The transcriptional benchmark dose (tBMD) and tBMD 95% lower limit (tBMDL) were used for quantitative analyses, which revealed three compound sub-groups that produced different types of differential gene expression; these groups of genes were mainly involved in inflammation, cholesterol biosynthesis, and oxidative stress. Furthermore, the tBMDL values for each test compound were in good agreement with the relevant no observed adverse effect level. These results indicate that our novel strategy for drug safety evaluation using mechanism-based classification and tBMDL would facilitate the application of toxicogenomics for risk assessment of cholestatic DILI.

New microRNA Biomarkers for Drug-Induced Steatosis and Their Potential to Predict the Contribution of Drugs to Non-alcoholic Fatty Liver Disease

Background and Aims: Drug-induced steatosis is a major reason for drug failure in clinical trials and post-marketing withdrawal; and therefore, predictive biomarkers are essential. These could be particularly relevant in non-alcoholic fatty liver disease (NAFLD), where most patients show features of the metabolic syndrome and are prescribed with combined chronic therapies, which can contribute to fatty liver. However, specific biomarkers to assess the contribution of drugs to NAFLD are lacking. We aimed to find microRNAs (miRNAs) responsive to steatotic drugs and to investigate if they could become circulating biomarkers for drug-induced steatosis.

Methods: Human HepG2 cells were treated with drugs and changes in miRNA levels were measured by microarray and qRT-PCR. Drug-induced fat accumulation in HepG2 was analyzed by high-content screening and enzymatic methods. miRNA biomarkers were also analyzed in the sera of 44 biopsy-proven NAFLD patients and in 10 controls.

Results: We found a set of 10 miRNAs [miR-22-5p, -3929, -24-2-5p, -663a, -29a-3p, -21 (5p and 3p), -27a-5p, -1260 and -202-3p] that were induced in human HepG2 cells and secreted to the culture medium upon incubation with model steatotic drugs (valproate, doxycycline, cyclosporin A and tamoxifen). Moreover, cell exposure to 17 common drugs for NAFLD patients showed that some of them (e.g., irbesartan, fenofibrate, and omeprazole) also induced these miRNAs and increased intracellular triglycerides, particularly in combinations. Finally, we found that most of these miRNAs (60%) were detected in human serum, and that NAFLD patients under fibrates showed both induction of these miRNAs and a more severe steatosis grade.

Conclusion: Steatotic drugs induce a common set of hepatic miRNAs that could be used in drug screening during preclinical development. Moreover, most of these miRNAs are serum circulating biomarkers that could become useful in the diagnosis of iatrogenic steatosis.

The Need for Biomarkers in Diagnosis and Prognosis of Drug-Induced Liver Disease: Does Metabolomics Have Any Role?

Drug-induced liver injury (DILI) is a potentially fatal adverse event and the leading cause of acute liver failure in the US and in the majority of Europe. The liver can be affected directly, in a dose-dependent manner, or idiosyncratically, independently of the dose, and therefore unpredictably. Currently, DILI is a diagnosis of exclusion that physicians should suspect in patients with unexplained elevated liver enzymes. Therefore, new diagnostic and prognostic biomarkers are necessary to achieve an early and reliable diagnosis of DILI and thus improve the prognosis. Although several DILI biomarkers have been found through analytical and genetic tests and pharmacokinetic approaches, none of them have been able to display enough specificity and sensitivity, so new approaches are needed. In this sense, metabolomics is a strongly and promising emerging field that, from biofluids collected through minimally invasive procedures, can obtain early biomarkers of toxicity, which may constitute specific indicators of liver damage.