Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of “first activation” as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail.

Understanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time-series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs’ mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.

Dissecting the Crosstalk Between Nrf2 and NF-κB Response Pathways in Drug-Induced Toxicity

Nrf2 and NF-κB are important regulators of the response to oxidative stress and inflammation in the body. Previous pharmacological and genetic studies have confirmed crosstalk between the two. The deficiency of Nrf2 elevates the expression of NF-κB, leading to increased production of inflammatory factors, while NF-κB can affect the expression of downstream target genes by regulating the transcription and activity of Nrf2. At the same time, many therapeutic drug-induced organ toxicities, including hepatotoxicity, nephrotoxicity, cardiotoxicity, pulmonary toxicity, dermal toxicity, and neurotoxicity, have received increasing attention from researchers in clinical practice. Drug-induced organ injury can destroy body function, reduce the patients’ quality of life, and even threaten the lives of patients. Therefore, it is urgent to find protective drugs to ameliorate drug-induced injury. There is substantial evidence that protective medications can alleviate drug-induced organ toxicity by modulating both Nrf2 and NF-κB signaling pathways. Thus, it has become increasingly important to explore the crosstalk mechanism between Nrf2 and NF-κB in drug-induced toxicity. In this review, we summarize the potential molecular mechanisms of Nrf2 and NF-κB pathways and the important effects on adverse effects including toxic reactions and look forward to finding protective drugs that can target the crosstalk between the two.

A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma

The utility of the Adverse Outcome Pathway (AOP) concept has been largely recognized by scientists, however, the AOP generation is still mainly done manually by screening through evidence and extracting probable associations. To accelerate this process and increase the reliability, we have developed an semi-automated workflow for AOP hypothesis generation. In brief, association mining methods were applied to high-throughput screening, gene expression, in vivo and disease data present in ToxCast and Comparative Toxicogenomics Database. This was supplemented by pathway mapping using Reactome to fill in gaps and identify events occurring at the cellular/tissue levels. Furthermore, in vivo data from TG-Gates was integrated to finally derive a gene, pathway, biochemical, histopathological and disease network from which specific disease sub-networks can be queried. To test the workflow, non-genotoxic-induced hepatocellular carcinoma (HCC) was selected as a case study. The implementation resulted in the identification of several non-genotoxic-specific HCC-connected genes belonging to cell proliferation, endoplasmic reticulum stress and early apoptosis. Biochemical findings revealed non-genotoxic-specific alkaline phosphatase increase. The explored non-genotoxic-specific histopathology was associated with early stages of hepatic steatosis, transforming into cirrhosis. This work illustrates the utility of computationally predicted constructs in supporting development by using pre-existing knowledge in a fast and unbiased manner.

Scaling laws of cell-fate responses to transient stress

Analysis and modelling of dose-survival curves of cells and tissues are often used to assess therapeutic efficacy or environmental risks, much less to infer the intracellular regulatory mechanisms of cellular stress response. However, systematic measurements of how cell survival depends on the time profile of stress, such as exposure duration, provide practical means to decipher the homeostatic dynamics of stress-response regulatory networks. In this paper, we propose a dynamical framework to theoretically address the relationship between cell fate response to a transient stress and the underlying regulatory feedback mechanisms. A simple network topology that couples a homeostatic negative feedback and a death-triggering positive feedback is shown to display four response regimes for which the iso-effect relationships between duration and intensity are captured by specific power laws. These distinct response regimes define several windows of stress duration for which lethality is not merely proportional to the product of intensity and duration, and, thus, for which cells are either more tolerant or more vulnerable to a given dose. Overall, this study highlights the differential roles of feedback strength, timescale and nonlinearity in promoting survivability to particular stress profiles, providing a valuable framework for a comparative analysis of diverse stress-specific regulatory networks.

Epoxyscillirosidine Induced Cytotoxicity and Ultrastructural Changes in a Rat Embryonic Cardiomyocyte (H9c2) Cell Line

Moraea pallida Bak. (yellow tulp) poisoning is the most important cardiac glycoside-induced intoxication in ruminants in South Africa. The toxic principle, 1α, 2α-epoxyscillirosidine, is a bufadienolide. To replace the use of sentient animals in toxicity testing, the aim of this study was to evaluate the cytotoxic effects of epoxyscillirosidine on rat embryonic cardiomyocytes (H9c2 cell line). This in vitro cell model can then be used in future toxin neutralization or toxico-therapy studies. Cell viability, evaluated with the methyl blue thiazol tetrazolium (MTT) assay, indicated a hormetic dose/concentration response, characterized by a biphasic low dose stimulation and high dose inhibition. Increased cell membrane permeability and leakage, as expected with necrotic cells, were demonstrated with the lactate dehydrogenase (LDH) assay. The LC₅₀ was 382.68, 132.28 and 289.23 µM for 24, 48, and 72 h respectively. Numerous cytoplasmic vacuoles, karyolysis and damage to the cell membrane, indicative of necrosis, were observed at higher doses. Ultra-structural changes suggested that the cause of H9c2 cell death, subsequent to epoxyscillirosidine exposure, is necrosis, which is consistent with myocardial necrosis observed at necropsy. Based on the toxicity observed, and supported by ultra-structural findings, the H9c2 cell line could be a suitable in vitro model to evaluate epoxyscillirosidine neutralization or other therapeutic interventions in the future.

A systematic analysis of Nrf2 pathway activation dynamics during repeated xenobiotic exposure

Oxidative stress leads to the activation of the Nuclear factor-erythroid-2-related factor 2 (Nrf2) pathway. While most studies have focused on the activation of the Nrf2 pathway after single chemical treatment, little is known about the dynamic regulation of the Nrf2 pathway in the context of repeated exposure scenarios. Here we employed single cell live imaging to quantitatively monitor the dynamics of the Nrf2 pathway during repeated exposure, making advantage of two HepG2 fluorescent protein reporter cell lines, expressing GFP tagged Nrf2 or sulfiredoxin 1 (Srxn1), a direct downstream target of Nrf2. High throughput live confocal imaging was used to measure the temporal dynamics of these two components of the Nrf2 pathway after repeated exposure to an extensive concentration range of diethyl maleate (DEM) and tert-butylhydroquinone (tBHQ). Single treatment with DEM or tBHQ induced Nrf2 and Srxn1 over time in a concentration-dependent manner. The Nrf2 response to a second treatment was lower than the response to the first exposure with the same concentration, indicating that the response is adaptive. Moreover, a limited fraction of individual cells committed themselves into the Nrf2 response during the second treatment. Despite the suppression of the Nrf2 pathway, the second treatment resulted in a three-fold higher Srxn1-GFP response compared to the first treatment, with all cells participating in the response. While after the first treatment Srxn1-GFP response was linearly related to Nrf2-GFP nuclear translocation, such a linear relationship was less clear for the second exposure. siRNA-mediated knockdown demonstrated that the second response is dependent on the activity of Nrf2. Several other, clinically relevant, compounds (i.e., sulphorophane, nitrofurantoin and CDDO-Me) also enhanced the induction of Srxn1-GFP upon two consecutive repeated exposure. Together the data indicate that adaptation towards pro-oxidants lowers the Nrf2 activation capacity, but simultaneously primes cells for the enhancement of an antioxidant response which depends on factors other than just Nrf2. These data provide further insight in the overall dynamics of stress pathway activation after repeated exposure and underscore the complexity of responses that may govern repeated dose toxicity.

Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or ‘modules’ enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.