A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors

Pluripotent stem cells for target organ developmental toxicity testing

Prenatal developmental toxicity research focuses on understanding the potential adverse effects of environmental agents, drugs, and chemicals on the development of embryos and fetuses. Traditional methods involve animal testing, but ethical concerns and the need for human-relevant models have prompted the exploration of alternatives. Pluripotent stem cells (PSCs) are versatile cells with the unique ability to differentiate into any cell type, serving as a foundational tool for studying human development. Two-dimensional (2D) PSC models are often chosen for their ease of use and reproducibility for high-throughput screening. However, they lack the complexity of an in vivo environment. Alternatively, three-dimensional (3D) PSC models, such as organoids, offer tissue architecture and intercellular communication more reminiscent of in vivo conditions. However, they are complicated to produce and analyze, usually requiring advanced and expensive techniques. This review discusses recent advances in the use of human PSCs differentiated into brain and heart lineages and emerging tools and methods that can be combined with PSCs to help address important scientific questions in the area of developmental toxicology. These advancements and new approach methods align with the push for more relevant and predictive developmental toxicity assessment, combining innovative techniques with organoid models to advance regulatory decision-making.

Assessment and Comparison of Early Developmental Toxicity of Six Per- and Polyfluoroalkyl Substances with Human Embryonic Stem Cell Models

Per- and polyfluoroalkyl substances (PFAS) are extensively utilized in varieties of products and tend to accumulate in the human body including umbilical cord blood and embryos/fetuses. In this study, we conducted an assessment and comparison of the potential early developmental toxicity of perfluorooctanoic acid (PFOA), undecafluorohexanoic acid (PFHxA), heptafluorobutyric acid, perfluorooctanesulfonate (PFOS), perfluorohexanesulfonate, and perfluorobutyric acid at noncytotoxic concentrations relevant to human exposure using models based on human embryonic stem cells in both three-dimensional embryoid body (EB) and monolayer differentiation configurations. All six compounds influenced the determination of cell fate by disrupting the expression of associated markers in both models and, in some instances, even led to alterations in the formation of cystic EBs. The expression of cilia-related gene IFT122 was significantly inhibited. Additionally, PFOS and PFOA inhibited ciliogenesis, while PFOA specifically reduced the cilia length. Transcriptome analysis revealed that PFOS altered 1054 genes and disrupted crucial signaling pathways such as WNT and TGF-β, which play integral roles in cilia transduction and are critical for early embryonic development. These results provide precise and comprehensive insights into the potential adverse health effects of these six PFAS compounds directly concerning early human embryonic development.

Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors

To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse toxicants caused similar patterns of gene regulation: the responses were dominated by cell de-differentiation and a triggering of canonical stress response pathways driven by ATF4 and NRF2. To obtain more detailed and specific information on the modes-of-action, the effects on energy metabolism (respiration and glycolysis) were measured. Ber, rotenone and MPP inhibited the mitochondrial respiratory chain and they shared complex I as the target. This group of toxicants was further evaluated by metabolomics under experimental conditions that did not deplete ATP. Ber (204 changed metabolites) showed similar effects as MPP and rotenone. The overall metabolic situation was characterized by oxidative stress, an over-abundance of NADH (>1000% increase) and a re-routing of metabolism in order to dispose of the nitrogen resulting from increased amino acid turnover. This unique overall pattern led to the accumulation of metabolites known as biomarkers of neurodegeneration (saccharopine, aminoadipate and branched-chain ketoacids). These findings suggest that neurotoxicity of mitochondrial inhibitors may result from an ensemble of metabolic changes rather than from a simple ATP depletion. The combi-omics approach used here provided richer and more specific MoA data than the more common transcriptomics analysis alone. As Ber, a human drug and food supplement, mimicked closely the mode-of-action of known neurotoxicants, its potential hazard requires further investigation.

Risk assessment of parabens in a transcriptomics-based in vitro test

Parabens have been used for decades as preservatives in food, drugs and cosmetics. The majority however, were banned in 2009 and 2014 leaving only methyl-, ethyl-, propyl-, and butyl-derivates available for subsequent use. Methyl- and propylparaben have been extensively tested in vivo, with no resulting evidence for developmental and reproductive toxicity (DART). In contrast, ethylparaben has not yet been tested for DART in animal experiments, and it is currently debated if additional animal studies are warranted. In order to perform a comparison of the four currently approved parabens, we used a previously established in vitro test based on human induced pluripotent stem cells (iPSC) that are exposed to test substances during their differentiation to neuroectodermal cells. EC50 values for cytotoxicity were 906 μM, 698 μM, 216 μM and 63 μM for methyl-, ethyl-, propyl- and butylparaben, respectively, demonstrating that cytotoxicity increases with increasing alkyl chain length. Genome-wide analysis demonstrated that FDR-adjusted significant gene expression changes occurred only at cytotoxic or close to cytotoxic concentrations, for example 1720 differentially expressed genes (DEG) at 1000 μM ethylparaben, 1 DEG at 316 μM, and no DEG at 100 μM or lower concentrations. The highest concentration of ethylparaben that did not induce any cytotoxicity nor DEG was 1670-fold above the highest concentration reported in biomonitoring studies (60 nM ethylparaben in cord blood). In conclusion, cytotoxicity and gene expression alterations of ethylparaben occurred at concentrations of approximately three orders of magnitude above human blood concentrations; moreover, the substance fitted well into a scenario where toxicity increases with the alkyl chain length, and gene expression changes only occur at cytotoxic or close to cytotoxic concentrations. Therefore, no evidence was obtained suggesting that in vivo DART with ethylparaben would lead to different results as the methyl- or propyl derivates.

Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing

BACKGROUND

The exact mechanisms of type 2 diabetes mellitus (T2DM) remain largely unknown. We intended to authenticate critical genes linked to T2DM progression by tandem single-cell sequencing and general transcriptome sequencing data.

METHODS

T2DM single-cell RNA-sequencing data were submitted by the Gene Expression Omnibus (GEO) database and ArrayExpress (EBI), from which gene expression matrices were retrieved. The common cell clusters and representative marker genes were ascertained by principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), CellMarker, and FindMarkers in two datasets (GSE86469 and GSE81608). T2DM-related differentially expressed marker genes were defined by intersection analysis of marker genes and GSE86468-differentially expressed genes. Receiver operating characteristic (ROC) curves were utilized to assign representative marker genes with diagnostic values by GSE86468, GSE29226 and external validation GSE29221, and their prospective target compounds were forecasted by PubChem. Besides, the R package clusterProfiler-based functional annotation was designed to unveil the intrinsic mechanisms of the target genes. At last, western blot was used to validate the alternation of CDKN1C and DLK1 expression in primary pancreatic islet cells cultured with or without 30mM glucose.

RESULTS

Three common cell clusters were authenticated in two independent T2DM single-cell sequencing data, covering neurons, epithelial cells, and smooth muscle cells. Functional ensemble analysis disclosed an intimate association of these cell clusters with peptide/insulin secretion and pancreatic development. Pseudo-temporal trajectory analysis indicated that almost all epithelial and smooth muscle cells were of neuron origin. We characterized CDKN1C and DLK1, which were notably upregulated in T2DM samples, with satisfactory availability in recognizing three representative marker genes in non-diabetic and T2DM samples, and they were also robustly interlinked with the clinical characteristics of patients. Western blot also demonstrated that, compared with control group, the expression of CDKN1C and DLK1 were increased in primary pancreatic islet cells cultured with 30 mM glucose for 48 h. Additionally, PubChem projected 11 and 21 potential compounds for CDKN1C and DLK1, respectively.

CONCLUSION

It is desirable that the emergence of the 2 critical genes indicated (CDKN1C and DLK1) could be catalysts for the investigation of the mechanisms of T2DM progression and the exploitation of innovative therapies.

Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across

The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.

Transcriptome meta-analysis of valproic acid exposure in human embryonic stem cells

Valproic acid (VPA) is a widely used antiepileptic drug not recommended in pregnancy because it is teratogenic. Many assays have assessed the impact of the VPA exposure on the transcriptome of human embryonic stem-cells (hESC), but the molecular perturbations that VPA exerts in neurodevelopment are not completely understood. This study aimed to perform a transcriptome meta-analysis of VPA-exposed hESC to elucidate the main biological mechanisms altered by VPA effects on the gene expression. Publicly available microarray and RNA-seq transcriptomes were selected in the Gene Expression Omnibus (GEO) repository. Samples were processed according to the standard pipelines for each technology in the Galaxy server and R. Meta-analysis was performed using the Fisher-P method. Overrepresented genes were obtained by evaluating ontologies, pathways, and phenotypes' databases. The meta-analysis performed in seven datasets resulted in 61 perturbed genes, 54 upregulated. Ontology and pathway enrichments suggested neurodevelopment and neuroinflammatory effects; phenotype overrepresentation included epilepsy-related genes, such as SCN1A and GABRB2. The NDNF gene upregulation was also identified; this gene is involved in neuron migration and survival during development. Sub-network analysis proposed TGFβ and BMP pathways activation. These results suggest VPA exerts effects in epilepsy-related genes even in embryonic cells. Neurodevelopmental genes, such as NDNF were upregulated and VPA might also disturb several development pathways. These mechanisms might help to explain the spectrum of VPA-induced congenital anomalies and the molecular effects on neurodevelopment.

Optimization of the TeraTox assay for preclinical teratogenicity assessment

Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives. To improve the sensitivity of human teratogenicity prediction, we characterized the TeraTox test, a newly developed multilineage differentiation assay using 3D human-induced pluripotent stem cells. TeraTox produces primary output concentration-dependent cytotoxicity and altered gene expression induced by each test compound. These data are fed into an interpretable machine-learning model to perform prediction, which relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Comparing TeraTox predictions with known human or animal toxicity, we report an accuracy of 69% (specificity: 53%, sensitivity: 79%). TeraTox performed better than 2 quantitative structure-activity relationship models and had a higher sensitivity than the murine embryonic stem cell test (accuracy: 58%, specificity: 76%, and sensitivity: 46%) run in the same laboratory. The overall prediction accuracy could be further improved by combining TeraTox and mouse embryonic stem cell test results. Furthermore, patterns of altered gene expression revealed by TeraTox may help grouping toxicologically similar compounds and possibly deducing common modes of action. The TeraTox assay and the dataset described here therefore represent a new tool and a valuable resource for drug teratogenicity assessment.

Classification of Developmental Toxicants in a Human iPSC Transcriptomics-Based Test

Despite the progress made in developmental toxicology, there is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration (C_max) and at 20-fold C_max for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold C_max) were used, in comparison to testing within the realistic clinical range (1-fold C_max). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants.

New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork

Gene-by-environment interactions are important for all facets of biology, especially behaviour. Families of isogenic strains of mice, such as the BXD strains, are excellently placed to study these interactions, as the same genome can be tested in multiple environments. BXD strains are recombinant inbred mouse strains derived from crossing two inbred strains—C57BL/6J and DBA/2J mice. Many reproducible genometypes can be leveraged, and old data can be reanalysed with new tools to produce novel insights. We obtained drug and behavioural phenotypes from Philip et al. Genes, Brain and Behaviour 2010, and reanalysed their data with new genotypes from sequencing, as well as new models (Genome-wide Efficient Mixed Model Association (GEMMA) and R/qtl2). We discovered QTLs on chromosomes 3, 5, 9, 11, and 14, not found in the original study. We reduced the candidate genes based on their ability to alter gene expression or protein function. Candidate genes included Slitrk6 and Cdk14. Slitrk6, in a Chromosome14 QTL for locomotion, was found to be part of a co-expression network involved in voluntary movement and associated with neuropsychiatric phenotypes. Cdk14, one of only three genes in a Chromosome5 QTL, is associated with handling induced convulsions after ethanol treatment, that is regulated by the anticonvulsant drug valproic acid. By using families of isogenic strains, we can reanalyse data to discover novel candidate genes involved in response to drugs of abuse.

DNA methylation changes associated with prenatal mercury exposure: A meta-analysis of prospective cohort studies from PACE consortium

Mercury (Hg) is a ubiquitous heavy metal that originates from both natural and anthropogenic sources and is transformed in the environment to its most toxicant form, methylmercury (MeHg). Recent studies suggest that MeHg exposure can alter epigenetic modifications during embryogenesis. In this study, we examined associations between prenatal MeHg exposure and levels of cord blood DNA methylation (DNAm) by meta-analysis in up to seven independent studies (n = 1462) as well as persistence of those relationships in blood from 7 to 8 year-old children (n = 794). In cord blood, we found limited evidence of differential DNAm at cg24184221 in MED31 (β = 2.28 × 10-4, p-value = 5.87 × 10-5) in relation to prenatal MeHg exposure. In child blood, we identified differential DNAm at cg15288800 (β = 0.004, p-value = 4.97 × 10-5), also located in MED31. This repeated link to MED31, a gene involved in lipid metabolism and RNA Polymerase II transcription function, may suggest a DNAm perturbation related to MeHg exposure that persists into early childhood. Further, we found evidence for association between prenatal MeHg exposure and child blood DNAm levels at two additional CpGs: cg12204245 (β = 0.002, p-value = 4.81 × 10-7) in GRK1 and cg02212000 (β = -0.001, p-value = 8.13 × 10-7) in GGH. Prenatal MeHg exposure was associated with DNAm modifications that may influence health outcomes, such as cognitive or anthropometric development, in different populations.

Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets

Large-scale microarray studies on post-mortem brain tissues have been utilized to investigate the complex molecular pathology of bipolar disorder. However, a major challenge in characterizing the dysregulation of gene expression in patients with bipolar disorder includes the lack of convergence between different studies, limiting comprehensive understanding from individual results. In this study, we aimed to identify genes that are both validated in published literature and are important classification features of unsupervised machine learning analysis of Stanley Brain Bank microarray database, followed by augmented intelligence method to identify distinct patient molecular subgroups. Through combining traditional literature approaches and machine learning, we identified TBL1XR1, SMARCA2, and CHMP5 to be replicated in 3 of the 4 studies included our analysis. The expression of these genes segregated unique subgroups of patients with bipolar disorder. Our study suggests the involvement of PPARγ pathway regulation in patients with bipolar disorder.

Stem Cell Based Preclinical Drug Development and Toxicity Prediction

Stem cell based toxicity prediction plays a very important role in the development of the drug. Unexpected adverse effects of the drugs during clinical trials are a major reason for the termination or withdrawal of drugs. Methods for predicting toxicity employ in vitro as well as in vivo models; however, the major drawback seen in the data derived from these animal models is the lack of extrapolation, owing to interspecies variations. Due to these limitations, researchers have been striving to develop more robust drug screening platforms based on stem cells. The application of stem cells based toxicity testing has opened up robust methods to study the impact of new chemical entities on not only specific cell types, but also organs. Pluripotent stem cells, as well as cells derived from them, can be evaluated for modulation of cell function in response to drugs. Moreover, the combination of state-of-the -art techniques such as tissue engineering and microfluidics to fabricate organ- on-a-chip, has led to assays which are amenable to high throughput screening to understand the adverse and toxic effects of chemicals and drugs. This review summarizes the important aspects of the establishment of the embryonic stem cell test (EST), use of stem cells, pluripotent, induced pluripotent stem cells and organoids for toxicity prediction and drug development.

Stem Cells for Next Level Toxicity Testing in the 21st Century

The call for a paradigm change in toxicology from the United States National Research Council in 2007 initiates awareness for the invention and use of human-relevant alternative methods for toxicological hazard assessment. Simple 2D in vitro systems may serve as first screening tools, however, recent developments infer the need for more complex, multicellular organotypic models, which are superior in mimicking the complexity of human organs. In this review article most critical organs for toxicity assessment, i.e., skin, brain, thyroid system, lung, heart, liver, kidney, and intestine are discussed with regards to their functions in health and disease. Embracing the manifold modes-of-action how xenobiotic compounds can interfere with physiological organ functions and cause toxicity, the need for translation of such multifaceted organ features into the dish seems obvious. Currently used in vitro methods for toxicological applications and ongoing developments not yet arrived in toxicity testing are discussed, especially highlighting the potential of models based on embryonic stem cells and induced pluripotent stem cells of human origin. Finally, the application of innovative technologies like organs-on-a-chip and genome editing point toward a toxicological paradigm change moves into action.

Development and validation of the TGx-HDACi transcriptomic biomarker to detect histone deacetylase inhibitors in human TK6 cells

Transcriptomic biomarkers can be used to inform molecular initiating and key events involved in a toxicant's mode of action. To address the limited approaches available for identifying epigenotoxicants, we developed and assessed a transcriptomic biomarker of histone deacetylase inhibition (HDACi). First, we assembled a set of ten prototypical HDACi and ten non-HDACi reference compounds. Concentration-response experiments were performed for each chemical to collect TK6 human lymphoblastoid cell samples after 4 h of exposure and to assess cell viability following a 20-h recovery period in fresh media. One concentration was selected for each chemical for whole transcriptome profiling and transcriptomic signature derivation, based on cell viability at the 24-h time point and on maximal induction of HDACi-response genes (RGL1, NEU1, GPR183) or cellular stress-response genes (ATF3, CDKN1A, GADD45A) analyzed by TaqMan qPCR assays after 4 h of exposure. Whole transcriptomes were profiled after 4 h exposures by Templated Oligo-Sequencing (TempO-Seq). By applying the nearest shrunken centroid (NSC) method to the whole transcriptome profiles of the reference compounds, we derived an 81-gene toxicogenomic (TGx) signature, referred to as TGx-HDACi, that classified all 20 reference compounds correctly using NSC classification and the Running Fisher test. An additional 4 HDACi and 7 non-HDACi were profiled and analyzed using TGx-HDACi to further assess classification performance; the biomarker accurately classified all 11 compounds, including 3 non-HDACi epigenotoxicants, suggesting a promising specificity toward HDACi. The availability of TGx-HDACi increases the diversity of tools that can facilitate mode of action analysis of toxicants using gene expression profiling.

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices.

Methylmercury Impact on Adult Neurogenesis: Is the Worst Yet to Come From Recent Brazilian Environmental Disasters?

Worldwide environmental tragedies of anthropogenic origin causing massive release of metals and other pollutants have been increasing considerably. These pollution outbreaks affect the ecosystems and impact human health. Among those tragedies, recent large-scale environmental disasters in Brazil strongly affected riverside populations, leading to high-risk exposure to methylmercury (MeHg). MeHg is highly neurotoxic to the developing brain. This toxicant causes neural stem cell dysfunction and neurodevelopmental abnormalities. However, less is known about the effects of MeHg in the postnatal neurogenic niche, which harbors neural stem cells and their progeny, in the adult brain. Therefore, taking in consideration the impact of MeHg in human health it is urgent to clarify possible associations between exposure to mercury, accelerated cognitive decline, and neurodegenerative diseases. In this perspectives paper, we discuss the neurotoxic mechanisms of MeHg on postnatal neurogenesis and the putative implications associated with accelerated brain aging and early-onset cognitive decline in populations highly exposed to this environmental neurotoxicant.

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.

Toxicity of mercury: Molecular evidence

Minamata disease in Japan and the large-scale poisoning by methylmercury (MeHg) in Iraq caused wide public concerns about the risk emanating from mercury for human health. Nowadays, it is widely known that all forms of mercury induce toxic effects in mammals, and increasing evidence supports the concern that environmentally relevant levels of MeHg could impact normal biological functions in wildlife. The information of mechanism involved in mercurial toxicity is growing but knowledge gaps still exist between the adverse effects and mechanisms of action, especially at the molecular level. A body of data obtained from experimental studies on mechanisms of mercurial toxicity in vivo and in vitro points to that disruption of the antioxidant system may play an important role in the mercurial toxic effects. Moreover, the accumulating evidence indicates that signaling transduction, protein or/and enzyme activity, and gene regulation are involving in mediating toxic and adaptive response to mercury exposure. We conducted here a comprehensive review of mercurial toxic effects on wildlife and human, in particular synthesized key findings of molecular pathways involved in mercurial toxicity from the cells to human. We discuss the molecular evidence related mercurial toxicity to the adverse effects, with particular emphasis on the gene regulation. The further studies relying on Omic analysis connected to adverse effects and modes of action of mercury will aid in the evaluation and validation of causative relationship between health outcomes and gene expression.

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox_(UKN). For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox_(UKN)) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This 'RoFA predictor gene set' may be used for a simplified and less costly setup of the STOP-tox_(UKN) assay.

Road Map for Development of Stem Cell-Based Alternative Test Methods

Much progress has been made in establishing strategies for differentiation of induced human pluripotent stem cells (hiPSCs). However, differentiated hiPSCs are not yet routinely used for prediction of toxicity. Here, limiting factors are summarised and possibilities for improvement are discussed, with a focus on hepatocytes, cardiomyocytes, tubular epithelial cells, and developmental toxicity. Moreover, we make recommendations for further fine-tuning of differentiation protocols for hiPSCs to hepatocyte-like cells by comparing individual steps of currently available protocols to the mechanisms occurring during embryonic development. A road map is proposed to facilitate test system development, including a description of the most useful performance metrics.

Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Histone acetylation plays a central role in gene regulation and is sensitive to the levels of metabolic intermediates. However, predicting the impact of metabolic alterations on acetylation in pathological conditions is a significant challenge. Here, we present a genome-scale network model that predicts the impact of nutritional environment and genetic alterations on histone acetylation. It identifies cell types that are sensitive to histone deacetylase inhibitors based on their metabolic state, and we validate metabolites that alter drug sensitivity. Our model provides a mechanistic framework for predicting how metabolic perturbations contribute to epigenetic changes and sensitivity to deacetylase inhibitors.

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.

In Vitro Pluripotent Stem Cell Differentiation to Hepatocyte Ceases Further Maturation at an Equivalent Stage of E15 in Mouse Embryonic Liver Development

Hepatocyte-like cells (HLCs) can be derived from pluripotent stem cells (PSCs) by sequential treatment of chemical cues to mimic the microenvironment of embryonic liver development. However, these HLCs do not reach the full maturity level of primary hepatocytes. In this study, we carried out a meta-analysis of cross-species transcriptome data of in vitro differentiation of human PSCs to HLCs and in vivo mouse embryonic liver development to identify the developmental stage at which HLC maturation was blocked at. Systematic variations were found associated with the data source and removed by batch correction. Using principal component analysis, HLCs from different stages of differentiation were aligned with mouse embryonic liver development chronologically. A "unified developmental time" (DT) scale was developed after aligning in vitro HLC differentiation and in vivo embryonic liver development. HLCs were found to cease further maturation at an equivalent stage of mouse embryonic day (E)13-15. Genes with discordant time dynamics were identified by aligning in vivo and in vitro data set onto a common DT scale. These genes may be targets of genetic intervention for enhancing the maturity of PSC-derived HLCs.

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

Background

System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.

Results

The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure.

Conclusions

The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users.

Recommendation on test readiness criteria for new approach methods in toxicology: Exemplified for developmental neurotoxicity

Multiple non-animal-based test methods have never been formally validated. In order to use such new approach methods (NAMs) in a regulatory context, criteria to define their readiness are necessary. The field of developmental neurotoxicity (DNT) testing is used to exemplify the application of readiness criteria. The costs and number of untested chemicals are overwhelming for in vivo DNT testing. Thus, there is a need for inexpensive, high-throughput NAMs, to obtain initial information on potential hazards, and to allow prioritization for further testing. A background on the regulatory and scientific status of DNT testing is provided showing different types of test readiness levels, depending on the intended use of data from NAMs. Readiness criteria, compiled during a stakeholder workshop, uniting scientists from academia, industry and regulatory authorities are presented. An important step beyond the listing of criteria, was the suggestion for a preliminary scoring scheme. On this basis a (semi)-quantitative analysis process was assembled on test readiness of 17 NAMs with respect to various uses (e.g. prioritization/screening, risk assessment). The scoring results suggest that several assays are currently at high readiness levels. Therefore, suggestions are made on how DNT NAMs may be assembled into an integrated approach to testing and assessment (IATA). In parallel, the testing state in these assays was compiled for more than 1000 compounds. Finally, a vision is presented on how further NAM development may be guided by knowledge of signaling pathways necessary for brain development, DNT pathophysiology, and relevant adverse outcome pathways (AOP).

Stage-specific metabolic features of differentiating neurons: Implications for toxicant sensitivity

Developmental neurotoxicity (DNT) may be induced when chemicals disturb a key neurodevelopmental process, and many tests focus on this type of toxicity. Alternatively, DNT may occur when chemicals are cytotoxic only during a specific neurodevelopmental stage. The toxicant sensitivity is affected by the expression of toxicant targets and by resilience factors. Although cellular metabolism plays an important role, little is known how it changes during human neurogenesis, and how potential alterations affect toxicant sensitivity of mature vs. immature neurons. We used immature (d0) and mature (d6) LUHMES cells (dopaminergic human neurons) to provide initial answers to these questions. Transcriptome profiling and characterization of energy metabolism suggested a switch from predominantly glycolytic energy generation to a more pronounced contribution of the tricarboxylic acid cycle (TCA) during neuronal maturation. Therefore, we used pulsed stable isotope-resolved metabolomics (pSIRM) to determine intracellular metabolite pool sizes (concentrations), and isotopically non-stationary ¹³C-metabolic flux analysis (INST ¹³C-MFA) to calculate metabolic fluxes. We found that d0 cells mainly use glutamine to fuel the TCA. Furthermore, they rely on extracellular pyruvate to allow continuous growth. This metabolic situation does not allow for mitochondrial or glycolytic spare capacity, i.e. the ability to adapt energy generation to altered needs. Accordingly, neuronal precursor cells displayed a higher sensitivity to several mitochondrial toxicants than mature neurons differentiated from them. In summary, this study shows that precursor cells lose their glutamine dependency during differentiation while they gain flexibility of energy generation and thereby increase their resistance to low concentrations of mitochondrial toxicants.

New animal-free concepts and test methods for developmental toxicity and peripheral neurotoxicity

The complex toxicological fields of repeat dose organ toxicity (RDT) and developmental and reproductive toxicity (DART) still require new concepts and approaches to achieve a fully animal-free safety assessment of chemicals. One novel approach is the generation of relevant human cell types from pluripotent stem cells, and the use of such cells for the establishment of phenotypic test methods. Due to their broad endpoints, such tests capture multiple types of toxicants, i.e. they are a readout for the activation of many adverse outcome pathways (AOPs). The 2016 Lush Science Prize was awarded for the development of one such assay, the PeriTox test, which uses human peripheral neurons generated from stem cells. The assay endpoints measure various cell functions, and these give information on the potential neurotoxicity and developmental neurotoxicity hazard of test compounds. The PeriTox test method has a high predictivity and sensitivity for peripheral neurotoxicants, and thus addresses the inherent challenges in pesticide testing and drug development. Data from the test can be obtained quickly and at a relatively high-throughput, and thus, the assay has the potential to replace animal-based safety assessment during early product development or for screening potential environmental toxicants.

Whole genome microarray analysis of neural progenitor C17.2 cells during differentiation and validation of 30 neural mRNA biomarkers for estimation of developmental neurotoxicity

Despite its high relevance, developmental neurotoxicity (DNT) is one of the least studied forms of toxicity. Current guidelines for DNT testing are based on in vivo testing and they require extensive resources. Transcriptomic approaches using relevant in vitro models have been suggested as a useful tool for identifying possible DNT-generating compounds. In this study, we performed whole genome microarray analysis on the murine progenitor cell line C17.2 following 5 and 10 days of differentiation. We identified 30 genes that are strongly associated with neural differentiation. The C17.2 cell line can be differentiated into a co-culture of both neurons and neuroglial cells, giving a more relevant picture of the brain than using neuronal cells alone. Among the most highly upregulated genes were genes involved in neurogenesis (CHRDL1), axonal guidance (BMP4), neuronal connectivity (PLXDC2), axonogenesis (RTN4R) and astrocyte differentiation (S100B). The 30 biomarkers were further validated by exposure to non-cytotoxic concentrations of two DNT-inducing compounds (valproic acid and methylmercury) and one neurotoxic chemical possessing a possible DNT activity (acrylamide). Twenty-eight of the 30 biomarkers were altered by at least one of the neurotoxic substances, proving the importance of these biomarkers during differentiation. These results suggest that gene expression profiling using a predefined set of biomarkers could be used as a sensitive tool for initial DNT screening of chemicals. Using a predefined set of mRNA biomarkers, instead of the whole genome, makes this model affordable and high-throughput. The use of such models could help speed up the initial screening of substances, possibly indicating alerts that need to be further studied in more sophisticated models.

Editor's Highlight: Development of Novel Neural Embryonic Stem CellTests for High-Throughput Screening of Embryotoxic Chemicals

There is a great demand for appropriate alternative methods to rapidly evaluate the developmental and reproductive toxicity of a wide variety of chemicals. We used the differentiation of mouse embryonic stem cells (mESCs) into cardiomyocytes as a basis for establishing a rapid and highly reproducible invitro embryotoxicity test known as the Hand1-Luc Embryonic Stem Cell Test (Hand1-Luc EST). In this study, we developed novel neural-Luc ESTs using two marker genes for neural development, tubulin beta-3 (Tubb3) and Reelin (Reln), and evaluated the capacity of these tests to predict developmental toxicity. In addition, we tested whether an integrated approach (a combination of neural-Luc ESTs and the Hand1-Luc EST) improved developmental toxicant detection. To perform our neural-Luc ESTs, we needed to generate stable transgenic mESCs with individual promoters linked to the luciferase gene, and to establish that similar changes in promoter activities and mRNA expression levels occur during neural differentiation. Based on the concentration-response curves of 15 developmental toxicants and 17 non-developmental toxic chemicals, we derived a prediction formula and assessed the capacity of this formula to predict developmental toxicity. Although both were highly sensitive and specific for predicting developmental toxicity, neural-Luc ESTs had similar predictive capacities. In contrast, neural-Luc ESTs and Hand1-Luc EST had significantly different predictive powers. As expected, the combination of these ESTs increased the sensitivity of developmental toxicant detection. These results demonstrate the convenience and the usefulness of this combination of ESTs as an alternative assay system for future toxicological and mechanistic studies of developmental toxicity.

Embryonic stem cells and the next generation of developmental toxicity testing

INTRODUCTION

The advent of stem cell technology has seen the establishment of embryonic stem cells (ESCs) as molecular model systems and screening tools. Although ESCs are nowadays widely used in research, regulatory implementation for developmental toxicity testing is pending. Areas Covered: This review evaluates the performance of current ESC, including human (h)ESC testing systems, trying to elucidate their potential for developmental toxicity testing. It shall discuss defining parameters and mechanisms, their relevance and contemplate what can realistically be expected. Crucially this includes the question of how to ascertain the quality of currently employed cell lines and tests based thereon. Finally, the use of hESCs will raise ethical concerns which should be addressed early on. Expert Opinion: While the suitability of (h)ESCs as tools for research and development goes undisputed, any routine use for developmental toxicity testing currently still seems premature. The reasons for this comprise inherent biological deficiencies as well as cell line quality and system validation. Overcoming these issues will require collaboration of scientists, test developers and regulators. Also, validation needs to be made worthwhile for academia. Finally we have to continuously rethink existing strategies, making room for improved testing and innovative approaches.