Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity

Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments

There is increasing interest to employ in vitro transcriptomics experiments in toxicological testing, for example to determine a point-of-departure (PoD) for chemical safety assessment. However current practices to derive PoD tend to utilise a single exposure time despite the importance of exposure time on the manifestation of toxicity caused by a chemical. Therefore it is important to investigate both concentration and exposure time to determine how these factors affect biological responses, and as a consequence, the derivation of PoDs. In this study, metabolically competent HepaRG cells were exposed to five known toxicants over a range of concentrations and time points for subsequent gene expression analysis, using a targeted RNA expression assay (TempO-Seq). A non-parametric factor-modelling approach was used to model the collective response of all significant genes, which exploited the interdependence of differentially expressed gene responses. This in turn allowed the determination of an isobenchmark response (isoBMR) curve for each chemical in a reproducible manner. For 2 of the 5 chemicals tested, the PoD was observed to vary by 0.5-1 log-order within the 48-h timeframe of the experiment. The approach and findings presented here clearly demonstrate the need to take both concentration and exposure time into account when designing in vitro toxicogenomics experiments to determine PoD. Doing so also provides a means to use concentration-time-response modelling as a basis to extrapolate a PoD from shorter to longer exposure durations, and to identify chemicals of concern that can cause cumulative effects over time.

Roles of transmembrane protein 135 in mitochondrial and peroxisomal functions - implications for age-related retinal disease

Aging is the most significant risk factor for age-related diseases in general, which is true for age-related diseases in the eye including age-related macular degeneration (AMD). Therefore, in order to identify potential therapeutic targets for these diseases, it is crucial to understand the normal aging process and how its mis-regulation could cause age-related diseases at the molecular level. Recently, abnormal lipid metabolism has emerged as one major aspect of age-related symptoms in the retina. Animal models provide excellent means to identify and study factors that regulate lipid metabolism in relation to age-related symptoms. Central to this review is the role of transmembrane protein 135 (TMEM135) in the retina. TMEM135 was identified through the characterization of a mutant mouse strain exhibiting accelerated retinal aging and positional cloning of the responsible mutation within the gene, indicating the crucial role of TMEM135 in regulating the normal aging process in the retina. Over the past decade, the molecular functions of TMEM135 have been explored in various models and tissues, providing insights into the regulation of metabolism, particularly lipid metabolism, through its action in multiple organelles. Studies indicated that TMEM135 is a significant regulator of peroxisomes, mitochondria, and their interaction. Here, we provide an overview of the molecular functions of TMEM135 which is crucial for regulating mitochondria, peroxisomes, and lipids. The review also discusses the age-dependent phenotypes in mice with TMEM135 perturbations, emphasizing the importance of a balanced TMEM135 function for the health of the retina and other tissues including the heart, liver, and adipose tissue. Finally, we explore the potential roles of TMEM135 in human age-related retinal diseases, connecting its functions to the pathobiology of AMD.

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens

With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.

Cellular mechanisms of biodegradable zinc and magnesium materials on promoting angiogenesis

Biodegradable metals, zinc and magnesium, have been regarded as next-generation, biomedical implant materials to promote tissue repair and regeneration. These implants might also promote the vascularization of surrounding neotissue. Released metallic ions, Zn²⁺ and Mg²⁺, show promise in vitro to implement vessel growth by stimulating the expression of pro-angiogenic cytokines, yet there is little known regarding how cellular responses transcend to influence the tissue environment. This study serves to optimize angiogenic behavior using EA.hy926 endothelial cultures exposed to Zn²⁺ and Mg²⁺ gradients and observe the translation of these effects on blood vessel development via the in ovo chorioallantoic membrane (CAM) assay. Findings indicate that Zn²⁺ 10 μM and Mg²⁺ 10 mM instigate the most prominent effects using endothelial cultures via scratch wound and tube formation assays, yet higher concentrations at Zn²⁺ 50 μM and Mg²⁺ 50 mM encourage significant angiogenesis along the CAM. Immunoblotting results also conclude the presence and upregulation of cytokines involved in vessel growth. Optimizing the angiogenic potential of Zn²⁺ and Mg²⁺ separately sheds light to design future engineering constructs for promoting blood vessel development and successful assimilation between host and implant tissue.

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.

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.

N-acetylcysteine alleviates high fat diet-induced hepatic steatosis and liver injury via regulating intestinal microecology in mice

N-acetylcysteine (NAC), a well-accepted antioxidant, has been shown to protect against high fat diet (HFD)-induced obesity-associated non-alcoholic fatty liver disease (NAFLD) in mice. However, the underlying mechanism(s) of the beneficial...

A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.

Temporal transcriptomic alterations of cadmium exposed human iPSC-derived renal proximal tubule-like cells

Cadmium is a well-studied environmental pollutant where the kidney and particularly the proximal tubule cells are especially sensitive as they are exposed to higher concentrations of cadmium than other tissues. Here we investigated the temporal transcriptomic alterations (TempO-Seq) of human induced pluripotent stem cell (iPSC)-derived renal proximal tubule-like (PTL) cells exposed to 5 μM cadmium chloride for 1, 2, 4, 8, 12, 16, 20, 24, 72 and 168 h. There was an early activation (within 4 h) of the metal and oxidative stress responses (metal-responsive transcription factor-1 (MTF1) and nuclear factor erythroid-2-related factor 2 (Nrf2) genes). The Nrf2 response returned to baseline within 24 h. The Activator Protein 1 (AP-1) regulated genes HSPA6 and FOSL-1 followed the Nrf2 time course. While the MTF1 genes also spiked at 4 h, they remained strongly elevated over the entire exposure period. The data and cell culture model utilised will be useful in further research aimed at the refinement of safe human exposure limits for cadmium, other metals and their mixtures.

TMEM135 is a Novel Regulator of Mitochondrial Dynamics and Physiology with Implications for Human Health Conditions

Transmembrane proteins (TMEMs) are integral proteins that span biological membranes. TMEMs function as cellular membrane gates by modifying their conformation to control the influx and efflux of signals and molecules. TMEMs also reside in and interact with the membranes of various intracellular organelles. Despite much knowledge about the biological importance of TMEMs, their role in metabolic regulation is poorly understood. This review highlights the role of a single TMEM, transmembrane protein 135 (TMEM135). TMEM135 is thought to regulate the balance between mitochondrial fusion and fission and plays a role in regulating lipid droplet formation/tethering, fatty acid metabolism, and peroxisomal function. This review highlights our current understanding of the various roles of TMEM135 in cellular processes, organelle function, calcium dynamics, and metabolism.

Palmitate and Fructose Interact to Induce Human Hepatocytes to Produce Pro-Fibrotic Transcriptional Responses in Hepatic Stellate Cells Exposed to Conditioned Media

BACKGROUND/AIMS

Excessive consumption of dietary fat and sugar is associated with an elevated risk of nonalcoholic fatty liver disease (NAFLD). Hepatocytes exposed to saturated fat or sugar exert effects on nearby hepatic stellate cells (HSCs); however, the mechanisms by which this occurs are poorly understood. We sought to determine whether paracrine effects of hepatocytes exposed to palmitate and fructose produced profibrotic transcriptional responses in HSCs.

METHODS

We performed expression profiling of mRNA and lncRNA from HSCs treated with conditioned media (CM) from human hepatocytes treated with palmitate (P), fructose (F), or both (PF).

RESULTS

In HSCs exposed to CM from palmitate-treated hepatocytes, we identified 374 mRNAs and 607 lncRNAs showing significant differential expression (log₂ foldchange ≥ |1|; FDR ≤0.05) compared to control cells. In HSCs exposed to CM from PF-treated hepatocytes, the number of differentially expressed genes was much higher (1198 mRNAs and 3348 lncRNAs); however, CM from fructose-treated hepatocytes elicited no significant changes in gene expression. Pathway analysis of differentially expressed genes showed enrichment for hepatic fibrosis and hepatic stellate cell activation in P- (FDR =1.30E-04) and PF-(FDR =9.24E-06)
groups. We observed 71 lncRNA/nearby mRNA pairs showing differential expression under PF conditions. There were 90 mRNAs and 264 lncRNAs strongly correlated between the PF group and differentially expressed transcripts from a comparison of activated and quiescent HSCs, suggesting that some of the transcriptomic changes occurring in response to PF overlap with HSC activation.

CONCLUSION

The results reported here have implications for dietary modifications in the prevention and treatment of NAFLD.

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ level toxicity end points (carcinogenicity, cardiotoxicity, developmental toxicity, hepatotoxicity, nephrotoxicity, neurotoxicity, reproductive toxicity, and skin toxicity). Integrated analysis of in vitro assay data, molecular targets and pathway annotations from the literature, and toxicity-molecular target associations derived from text mining, combined with machine learning techniques, were used to generate molecular targets for each of the organ level toxicity end points. A total of 1516 toxicity-related genes were identified and subsequently analyzed for biological pathway coverage, resulting in 206 significant pathways (p-value <0.05), ranging from 3 (e.g., developmental toxicity) to 101 (e.g., skin toxicity) for each toxicity end point. This study presents a systematic and comprehensive analysis of molecular targets and pathways related to various in vivo toxicity end points. These molecular targets and pathways could aid in understanding the biological mechanisms of toxicity and serve as a guide for the design of suitable in vitro assays for more efficient toxicity testing. In addition, these results are complementary to the existing adverse outcome pathway (AOP) framework and can be used to aid in the development of novel AOPs. Our results provide abundant testable hypotheses for further experimental validation.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.