Toxicogenomic biomarkers for liver toxicity

Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity

Gene expression biomarkers have the potential to identify genotoxic and non-genotoxic carcinogens, providing opportunities for integrated testing and reducing animal use. In August 2022, an International Workshops on Genotoxicity Testing (IWGT) workshop was held to critically review current methods to identify genotoxicants using transcriptomic profiling. Here, we summarize the findings of the workgroup on the state of the science regarding the use of transcriptomic biomarkers to identify genotoxic chemicals in vitro and in vivo. A total of 1341 papers were examined to identify the biomarkers that show the most promise for identifying genotoxicants. This analysis revealed two independently derived in vivo biomarkers and three in vitro biomarkers that, when used in conjunction with standard computational techniques, can identify genotoxic chemicals in vivo (rat or mouse liver) or in human cells in culture using different gene expression profiling platforms, with predictive accuracies of ≥92%. These biomarkers have been validated to differing degrees but typically show high reproducibility across transcriptomic platforms and model systems. They offer several advantages for applications in different contexts of use in genotoxicity testing including: early signal detection, moderate-to-high-throughput screening capacity, adaptability to different cell types and tissues, and insights on mechanistic information on DNA-damage response. Workshop participants agreed on consensus statements to advance the regulatory adoption of transcriptomic biomarkers for genotoxicity. The participants agreed that transcriptomic biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies in vitro and using short-term rodent exposures to identify genotoxic and non-genotoxic chemicals that may cause cancer and heritable genetic effects. Following are the consensus statements from the workgroup. Transcriptomic biomarkers for genotoxicity can be used in Weight of Evidence (WoE) evaluation to: determine potential genotoxic mechanisms and hazards; identify misleading positives from in vitro genotoxicity assays; serve as new approach methodologies (NAMs) integrated into the standard battery of genotoxicity tests. Several transcriptomic biomarkers have been developed from sufficiently robust training data sets, validated with external test sets, and have demonstrated performance in multiple laboratories. These transcriptomic biomarkers can be used following established study designs and models designated through existing validation exercises in WoE evaluation. Bridging studies using a selection of training and test chemicals are needed to deviate from the established protocols to confirm performance when a transcriptomic biomarker is being applied in other: tissues, cell models, or gene expression platforms. Top dose selection and time of gene expression analysis are critical and should be established during transcriptomic biomarker development. These conditions are the only ones suited for transcriptomic biomarker use unless additional bridging or pharmacokinetic studies are conducted. Temporal effects for genotoxicants that operate via distinct mechanisms should be considered in data interpretation. Fixed transcriptomic biomarker gene sets and analytical processes do not need to be independently rederived in biomarker validation. Validation should focus on the performance of the gene set in external test sets. Robust external testing should ensure a minimum of additional chemicals spanning genotoxic and non-genotoxic modes of action. Genes in the transcriptomic biomarker do not need to be known to be mechanistically involved in genotoxicity responses. Existing frameworks described for NAMs could be applied for validation of transcriptomic biomarkers. Reproducibility of bioinformatic analysis is critical for the regulatory application of transcriptomic biomarkers. A bioinformatics expert should be involved with creating reproducible methods for the qualification and application of each transcriptomic biomarker.

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

Myocardial toxicity induced by silica nanoparticles in a transcriptome profile

The deleterious effects of silica nanoparticles (SiNPs) on human health and the ecological system have gradually gained attention owing to their heavy annual output and extensive global flux. The updated epidemiological or experimental investigations have demonstrated the potential myocardial toxicity triggered by SiNPs, but the underlying mechanisms and long-lasting cardiac effects are still poorly understood. Here, a rat model of sub-chronic respiratory exposure to SiNPs was conducted, and the histopathological analysis and ultrastructural investigation of heart tissues were carried out. More importantly, a comprehensive analysis of whole-genome transcription was utilized in rat heart to uncover key biological and cellular mechanisms triggered by SiNPs. The widening of myocardial space and partial fiber rupture were clearly manifested in rat heart after prolonged SiNPs exposure, particularly accompanied by mitochondrial swelling and cristae rupture. With the aid of Affymetrix GeneChips, 3153 differentially expressed genes (DEGs) were identified after SiNPs exposure, including 1916 down- and 1237 up-regulated genes. GO and KEGG analysis illustrated many important biological processes and pathways perturbed by SiNPs, mainly specializing in cellular stress, energy metabolism, actin filament dynamics and immune response. Signal-net analysis revealed that Prkaca (PKA) plays a core role in the cardiac toxification process of prolonged exposure of SiNPs to rats. Furthermore, qRT-PCR verified that PKA-mediated calcium signaling is probably responsible for SiNPs-induced cardiac injury. Conclusively, our study revealed that SiNPs caused myocardial injury, and particularly, provided transcriptomic insight into the role of PKA-calcium signaling triggered by SiNPs, which would facilitate SiNPs-based nanosafety assessment and biomedicine development.

Emerging Bioinformatics Methods and Resources in Drug Toxicology

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Identification of Translational microRNA Biomarker Candidates for Ketoconazole-Induced Liver Injury Using Next-Generation Sequencing

Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Reliable and translational biomarkers are needed for early detection of DILI. microRNAs (miRNAs) have received wide attention as a novel class of potential DILI biomarkers. However, it is unclear how DILI drugs other than acetaminophen may influence miRNA expression or which miRNAs could serve as useful biomarkers in humans. We selected ketoconazole (KCZ), a classic hepatotoxin, to study miRNA biomarkers for DILI as a proof of concept for a workflow that integrated in vivo, in vitro, and bioinformatics analyses. We examined hepatic miRNA expression in KCZ-treated rats at multiple doses and durations using miRNA-sequencing and correlated our results with conventional DILI biomarkers such as liver histology. Significant dysregulation of rno-miR-34a-5p, rno-miR-331-3p, rno-miR-15b-3p, and rno-miR-676 was associated with cytoplasmic vacuolization, a phenotype in rat livers with KCZ-induced injury, which preceded the elevation of serum liver transaminases (ALT and AST). Between rats and humans, miR-34a-5p, miR-331-3p, and miR-15b-3p were evolutionarily conserved with identical sequences, whereas miR-676 showed 73% sequence similarity. Using quantitative PCR, we found that the levels of hsa-miR-34a-5p, hsa-miR-331-3p, and hsa-miR-15b-3p were significantly elevated in the culture media of HepaRG cells treated with 100 µM KCZ (a concentration that induced cytotoxicity). Additionally, we computationally characterized the miRNA candidates for their gene targeting, target functions, and miRNA/target evolutionary conservation. In conclusion, we identified miR-34a-5p, miR-331-3p, and miR-15b-3p as translational biomarker candidates for early detection of KCZ-induced liver injury with a workflow applicable to computational toxicology studies.

Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles

The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic-induced injury. Xenobiotic-induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine-learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.

Iron overload by Superparamagnetic Iron Oxide Nanoparticles is a High Risk Factor in Cirrhosis by a Systems Toxicology Assessment

Superparamagnetic iron oxide nanoparticles (SPIONs) as a contrast agent have been widely used in magnetic resonance imaging for tumor diagnosis and theranostics. However, there has been safety concern of SPIONs with cirrhosis related to excess iron-induced oxidative stress. In this study, the impact of iron overload by SPIONs was assessed on a mouse cirrhosis model. A single dose of SPION injection at 0.5 or 5 mg Fe/kg in the cirrhosis group induced a septic shock response at 24 h with elevated serum levels of liver and kidney function markers and extended impacts over 14 days including high levels of serum cholesterols and persistent low serum iron level. In contrast, full restoration of liver functions was found in the normal group with the same dosages over time. Analysis with PCR array of the toxicity pathways revealed the high dose of SPIONs induced significant expression changes of a distinct subset of genes in the cirrhosis liver. All these results suggested that excess iron of the high dose of SPIONs might be a risk factor for cirrhosis because of the marked impacts of elevated lipid metabolism, disruption of iron homeostasis and possibly, aggravated loss of liver functions.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Developing a practical toxicogenomics data analysis system utilizing open-source software

Comprehensive gene expression analysis has been applied to investigate the molecular mechanism of toxicity, which is generally known as toxicogenomics (TGx). When analyzing large-scale gene expression data obtained by microarray analysis, typical multivariate data analysis methods performed with commercial software such as hierarchical clustering or principal component analysis usually do not provide conclusive outputs by themselves. To best utilize the TGx data for toxicity evaluation in the drug development process, fit-for-purpose customization of the analytical algorithm with user-friendly interface and intuitive outputs are required to practically address the toxicologists' demands. However, commercial software is usually not very flexible in the customization of their functions or outputs. Owing to the recent advancement and accumulation of open-source software contributed by bioinformaticians all over the world, it becomes easier for us to develop practical and fit-for-purpose analytical software by ourselves with fairly low cost and efforts. The aim of this article is to present an example of developing an automated TGx data processing system (ATP system), which implements gene set-level analysis toxicogenomic profiling by D-score method and generates straightforward output that makes it easy to interpret the biological and toxicological significance of the TGx data. Our example will provide basic clues for readers to develop and customize their own TGx data analysis system which complements the function of existing commercial software.

The impact of network biology in pharmacology and toxicology

With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

Toxicogenomic investigation on rat testicular toxicity elicited by 1,3-dinitrobenzene

Rats were treated with a single oral dose of 10, 25 and 50mg/kg of 1,3-dinitrobenzene (DNB), and the testis was subjected to a GeneChip microarray analysis. A total of 186 and 304 gene probe sets were up- and down-regulated, respectively, by the DNB treatment, where spermatocyte death and Sertoli cell vacuolation in testis and increased debris of spermatogenic cell in epididymis were noted. The expression profile for four sets of genes were investigated, whose expressions are reported to localize in specific cell types in the seminiferous epithelium, namely Sertoli cells, spermatogonia plus early spermtocytes, pachytene spermatocytes and round spermatids. The data demonstrated that pachytene spermatocyte-specific genes elicited explicit down-regulation in parallel with the progression of spermatocyte death, while other gene sets did not show characteristic expression changes. In addition, Gene Ontology analysis indicated that genes associated with cell adhesion-related genes were significantly enriched in the up-regulated genes following DNB treatment. Cell adhesion-related genes, namely Cdh2, Ctnna1, Vcl, Zyx, Itgb1, Testin, Lamc3, Pvrl2 and Gsn, showed an increase in microarray and the up-regulation of Cdh2 and Testin were confirmed by real time RT-PCR. The gene expression changes of pachytene spermatocyte-specific genes and cell adhesion-related genes were thought to reflect a decrease in the number of spermatocytes and dysfunction of Sertoli-germ cells adhesion junction, and therefore these genes would be potential genomic biomarkers for assessing DNB-type testicular toxicity.

Successful drug development despite adverse preclinical findings part 2: examples

To illustrate the process of addressing adverse preclinical findings (APFs) as outlined in the first part of this review, a number of cases with unexpected APF in toxicity studies with drug candidates is discussed in this second part. The emphasis is on risk characterization, especially regarding the mode of action (MoA), and risk evaluation regarding relevance for man. While severe APFs such as retinal toxicity may turn out to be of little human relevance, minor findings particularly in early toxicity studies, such as vasculitis, may later pose a real problem. Rodents are imperfect models for endocrine APFs, non-rodents for human cardiac effects. Liver and kidney toxicities are frequent, but they can often be monitored in man and do not necessarily result in early termination of drug candidates. Novel findings such as the unusual lesions in the gastrointestinal tract and the bones presented in this review can be difficult to explain. It will be shown that well known issues such as phospholipidosis and carcinogenicity by agonists of peroxisome proliferator-activated receptors (PPAR) need to be evaluated on a case-by-case basis. The latter is of particular interest because the new PPAR α and dual α/γ agonists resulted in a change of the safety paradigm established with the older PPAR α agonists. General toxicologists and pathologists need some understanding of the principles of genotoxicity and reproductive toxicity testing. Both types of preclinical toxicities are major APF and clinical monitoring is difficult, generally leading to permanent use restrictions.

Practical application of toxicogenomics for profiling toxicant-induced biological perturbations

A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene expression analysis or toxicogenomics investigation is highly advantageous. The recent accumulation of toxicity-associated gene sets (toxicogenomic biomarkers), enrichment in public or commercial large-scale microarray database and availability of open-source software resources facilitate our utilization of the toxicogenomic data. However, toxicologists, who are usually not experts in computational sciences, tend to be overwhelmed by the gigantic amount of data. In this paper we present practical applications of toxicogenomics by utilizing biomarker gene sets and a simple scoring method by which overall gene set-level expression changes can be evaluated efficiently. Results from the gene set-level analysis are not only an easy interpretation of toxicological significance compared with individual gene-level profiling, but also are thought to be suitable for cross-platform or cross-institutional toxicogenomics data analysis. Enrichment in toxicogenomics databases, refinements of biomarker gene sets and scoring algorithms and the development of user-friendly integrative software will lead to better evaluation of toxicant-elicited biological perturbations.

The future of toxicity testing

In 2007, the U.S. National Research Council (NRC) released a report, "Toxicity Testing in the 21st Century: A Vision and a Strategy," that proposes a paradigm shift for toxicity testing of environmental agents. The vision is based on the notion that exposure to environmental agents leads to adverse health outcomes through the perturbation of toxicity pathways that are operative in humans. Implementation of the NRC vision will involve a fundamental change in the assessment of toxicity of environmental agents, moving away from adverse health outcomes observed in experimental animals to the identification of critical perturbations of toxicity pathways. Pathway perturbations will be identified using in vitro assays and quantified for dose response using methods in computational toxicology and other recent scientific advances in basic biology. Implementation of the NRC vision will require a major research effort, not unlike that required to successfully map the human genome, extending over 10 to 20 years, involving the broad scientific community to map important toxicity pathways operative in humans. This article provides an overview of the scientific tools and technologies that will form the core of the NRC vision for toxicity testing. Of particular importance will be the development of rapidly performed in vitro screening assays using human cells and cell lines or human tissue surrogates to efficiently identify environmental agents producing critical pathway perturbations. In addition to the overview of the NRC vision, this study documents the reaction by a number of stakeholder groups since 2007, including the scientific, risk assessment, regulatory, and animal welfare communities.

Scoring multiple toxicological endpoints using a toxicogenomic database

As information regarding microarray data sets and toxicogenomic biomarkers grows rapidly, the process of analyzing data and interpreting the results is increasingly complicated. To facilitate data analysis, a simple expression ratio-based scoring method called the TGP1 score was previously proposed [Kiyosawa, N., Shiwaku, K., Hirode, M., Omura, K., Uehara, T., Shimizu, T., Mizukawa, Y., Miyagishima, T., Ono, A., Nagao, T., Urushidani, T., 2006. Utilization of a one-dimensional score for surveying chemical-induced changes in expression levels of multiple biomarker gene sets using a large-scale toxicogenomics database. J. Toxicol. Sci. 31, 433-448]. Although the TGP1 score has demonstrated its efficacy for rapid comprehension of large-scale toxicogenomic data sets, inclusion of low quality gene expression data in the biomarker gene set produced flaws in the calculated score. To overcome this shortcoming, we tested a new scoring method called the differentially expressed gene score (D-score), where Detection Call as well as signal log ratios generated by MAS5 algorithm on Affymetrix GeneChip data were considered for the calculation. Four prototypical toxicants, namely acetaminophen, phenobarbital, clofibrate and acetamidofluorene, were used for detailed analysis. A toxicogenomics database (TG-GATEs) was utilized as a reference data set. The D-score successfully alleviated the effects of low quality data on the score calculation, and captured the overall direction of expression changes as well as the magnitude of expression change level of a set of genes, highlighting the affected toxicological endpoints elicited by chemical treatment. The D-score will be useful for high-throughput toxicity screening using a toxicogenomic database and biomarkers.