Comparison Of Observation-Based And Model-Based Identification Of Alert Concentrations From Concentration-Expression Data.

MOTIVATION: An important goal of concentration-response studies in toxicology is to determine an 'alert' concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phase I trial of ATM inhibitor M3541 in combination with palliative radiotherapy in patients with solid tumors.

BACKGROUND: Ataxia telangiectasia mutated (ATM) kinase orchestrates DNA double strand break (DSB) repair; ATM inhibitors may therefore enhance the therapeutic effect of DSB-inducing treatments such as radiotherapy (RT). M3541 is an orally administered selective inhibitor of ATM. METHODS: This phase I dose-escalation study evaluated the maximum-tolerated dose (MTD), recommended phase II dose(s) (RP2D), safety, pharmacokinetics (PK) and antitumor activity of M3541 in combination with fractionated palliative RT in patients with solid tumors. Fifteen patients received palliative RT (30 Gy in 10 fractions) and escalating doses of M3541 (50-300 mg administered on RT fraction days) guided by a Bayesian 2-parameter logistic regression model with overdose control. RESULTS: Doses of M3541 up to 300 mg/fraction day were well tolerated. One patient (200 mg group) experienced two dose-limiting toxicities (urinary tract infection, febrile neutropenia) that resolved with antibiotics. All patients reported ≥ 1 treatment-emergent adverse event (TEAE) but none led to treatment discontinuation. No grade ≥ 4 TEAEs were reported and there was no indication of a dose effect for any TEAE. Three patients (20.0%; 95% confidence interval 4.3-48.1) had confirmed complete or partial response. M3541 total plasma levels did not increase with dose following single or repeated dosing. No relationship was observed between dose and changes in the ratio of phosphorylated to total ATM or in immune cell counts. CONCLUSIONS: The MTD and RP2D could not be established as the study closed early due to the absence of a dose-response relationship and non-optimal PK profile. No further clinical development of M3541 was pursued. (Trial registration number ClinicalTrials.gov NCT03225105. Registration date July 21, 2017).

Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.

There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice.

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations.

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.

Toxicogenomics directory of rat hepatotoxicants in vivo and in cultivated hepatocytes.

Transcriptomics is developing into an invaluable tool in toxicology. The aim of this study was, using a transcriptomics approach, to identify genes that respond similar to many different chemicals (including drugs and industrial compounds) in both rat liver in vivo and in cultivated hepatocytes. For this purpose, we analyzed Affymetrix microarray expression data from 162 compounds that were previously tested in a concentration-dependent manner in rat livers in vivo and in rat hepatocytes cultivated in sandwich culture. These data were obtained from the Japanese Toxicogenomics Project (TGP) and North Rhine-Westphalian (NRW) data sets, which represent 138 and 29 compounds, respectively, and have only 5 compounds in common between them. The in vitro gene expression data from the NRW data set were generated in the present study, while TGP is publicly available. For each of the data sets, the overlap between up- or down-regulated genes in vitro and in vivo was identified, and named in vitro-in vivo consensus genes. Interestingly, the in vivo-in vitro consensus genes overlapped to a remarkable extent between both data sets, and were 21-times (upregulated genes) or 12-times (down-regulated genes) enriched compared to random expectation. Finally, the genes in the TGP and NRW overlap were used to identify the upregulated genes with the highest compound coverage, resulting in a seven-gene set of Cyp1a1, Ugt2b1, Cdkn1a, Mdm2, Aldh1a1, Cyp4a3, and Ehhadh. This seven-gene set was then successfully tested with structural analogues of valproic acid that are not present in the TGP and NRW data sets. In conclusion, the seven-gene set identified in the present study responds similarly in vitro and in vivo to a wide range of different chemicals. Despite these promising results with the seven-gene set, transcriptomics with cultivated rat hepatocytes remains a challenge, because in general many genes are up- or downregulated by in vitro culture per se, respond differently to test compounds in vitro and in vivo, and/or show higher variability in the in vitro system compared to the corresponding in vivo data.

Reaching the limits of prognostication in non-small cell lung cancer: an optimized biomarker panel fails to outperform clinical parameters.

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.

Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity.

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

Impairment of human neural crest cell migration by prolonged exposure to interferon-beta.

Human cell-based toxicological assays have been used successfully to detect known toxicants, and to distinguish them from negative controls. However, there is at present little experience on how to deal with hits from screens of compounds with yet unknown hazard. As a case study to this issue, we characterized human interferon-beta (IFNß) as potential developmental toxicant affecting neural crest cells (NCC). The protein was identified as a hit during a screen of clinically used drugs in the 'migration inhibition of neural crest' (MINC) assay. Concentration-response studies in the MINC combined with immunocytochemistry and mRNA quantification of cellular markers showed that IFNß inhibited NCC migration at concentrations as low as 20 pM. The effective concentrations found here correspond to levels found in human plasma, and they were neither cytostatic nor cytotoxic nor did they did they affect the differentiation state and overall phenotype of NCC. Data from two other migration assays confirmed that picomolar concentration of IFNß reduced the motility of NCC, while other interferons were less potent. The activation of JAK kinase by IFNß, as suggested by bioinformatics analysis of the transcriptome changes, was confirmed by biochemical methods. The degree and duration of pathway activation correlated with the extent of migration inhibition, and pharmacological block of this signaling pathway before, or up to 6 h after exposure to the cytokine prevented the effects of IFNß on migration. Thus, the reduction of vital functions of human NCC is a hitherto unknown potential hazard of endogenous or pharmacologically applied interferons.

Definition of transcriptome-based indices for quantitative characterization of chemically disturbed stem cell development: introduction of the STOP-Toxukn and STOP-Toxukk tests.

Stem cell-based in vitro test systems can recapitulate specific phases of human development. In the UKK test system, human pluripotent stem cells (hPSCs) randomly differentiate into cells of the three germ layers and their derivatives. In the UKN1 test system, hPSCs differentiate into early neural precursor cells. During the normal differentiation period (14 days) of the UKK system, 570 genes [849 probe sets (PSs)] were regulated >fivefold; in the UKN1 system (6 days), 879 genes (1238 PSs) were regulated. We refer to these genes as 'developmental genes'. In the present study, we used genome-wide expression data of 12 test substances in the UKK and UKN1 test systems to understand the basic principles of how chemicals interfere with the spontaneous transcriptional development in both test systems. The set of test compounds included six histone deacetylase inhibitors (HDACis), six mercury-containing compounds ('mercurials') and thalidomide. All compounds were tested at the maximum non-cytotoxic concentration, while valproic acid and thalidomide were additionally tested over a wide range of concentrations. In total, 242 genes (252 PSs) in the UKK test system and 793 genes (1092 PSs) in the UKN1 test system were deregulated by the 12 test compounds. We identified sets of 'diagnostic genes' appropriate for the identification of the influence of HDACis or mercurials. Test compounds that interfered with the expression of developmental genes usually antagonized their spontaneous development, meaning that up-regulated developmental genes were suppressed and developmental genes whose expression normally decreases were induced. The fraction of compromised developmental genes varied widely between the test compounds, and it reached up to 60 %. To quantitatively describe disturbed development on a genome-wide basis, we recommend a concept of two indices, 'developmental potency' (D p) and 'developmental index' (D i), whereby D p is the fraction of all developmental genes that are up- or down-regulated by a test compound, and D i is the ratio of overrepresentation of developmental genes among all genes deregulated by a test compound. The use of D i makes hazard identification more sensitive because some compounds compromise the expression of only a relatively small number of genes but have a high propensity to deregulate developmental genes specifically, resulting in a low D p but a high D i. In conclusion, the concept based on the indices D p and D i offers the possibility to quantitatively express the propensity of test compounds to interfere with normal development.

Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration.

The in vitro test battery of the European research consortium ESNATS ('novel stem cell-based test systems') has been used to screen for potential human developmental toxicants. As part of this effort, the migration of neural crest (MINC) assay has been used to evaluate chemical effects on neural crest function. It identified some drug-like compounds in addition to known environmental toxicants. The hits included the HSP90 inhibitor geldanamycin, the chemotherapeutic arsenic trioxide, the flame-retardant PBDE-99, the pesticide triadimefon and the histone deacetylase inhibitors valproic acid and trichostatin A. Transcriptome changes triggered by these substances in human neural crest cells were recorded and analysed here to answer three questions: (1) can toxicants be individually identified based on their transcript profile; (2) how can the toxicity pattern reflected by transcript changes be compacted/dimensionality-reduced for practical regulatory use; (3) how can a reduced set of biomarkers be selected for large-scale follow-up? Transcript profiling allowed clear separation of different toxicants and the identification of toxicant types in a blinded test study. We also developed a diagrammatic system to visualize and compare toxicity patterns of a group of chemicals by giving a quantitative overview of altered superordinate biological processes (e.g. activation of KEGG pathways or overrepresentation of gene ontology terms). The transcript data were mined for potential markers of toxicity, and 39 transcripts were selected to either indicate general developmental toxicity or distinguish compounds with different modes-of-action in read-across. In summary, we found inclusion of transcriptome data to largely increase the information from the MINC phenotypic test.

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

Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of 'mercurials' (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

Toxicogenomics directory of chemically exposed human hepatocytes.

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory ( http://wiki.toxbank.net/toxicogenomics-map/ ) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes.

Mechanical strain mimicking breathing amplifies alterations in gene expression induced by SiO2 NPs in lung epithelial cells.

The effects of engineered nanomaterials on human health are still intensively studied in order to facilitate their safe application. However, relatively little is known how mechanical strain as induced in alveolar epithelial cells by breathing movements modifies biological responses to nanoparticles (NPs). In this study, A549 cells as a model for alveolar epithelial cells were exposed to 25 nm amorphous colloidal silica NPs under dynamic and static culture conditions. Gene array data, qPCR, and ELISA revealed an amplified effect of NPs when cells were mechanically stretched in order to model the physiological mechanical deformation during breathing. In contrast, treatment of cells with either strain or NPs alone only led to minor changes in gene expression or interleukin-8 (IL-8) secretion. Confocal microscopy revealed that stretching does not lead to an increased internalization of NPs, indicating that elevated intracellular NP accumulation is not responsible for the observed effect. Gene expression alterations induced by combined exposure to NPs and mechanical strain showed a high similarity to those known to be induced by TNF-α. This study suggests that the inclusion of mechanical strain into in vitro models of the human lung may have a strong influence on the test results.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

Glycerol-3-phosphate Acyltransferase 1 Promotes Tumor Cell Migration and Poor Survival in Ovarian Carcinoma.

Glycerophosphodiesterase EDI3 (GPCPD1; GDE5; GDPD6) has been suggested to promote cell migration, adhesion, and spreading, but its mechanisms of action remain uncertain. In this study, we targeted the glycerol-3-phosphate acyltransferase GPAM along with choline kinase-α (CHKA), the enzymes that catabolize the products of EDI3 to determine which downstream pathway is relevant for migration. Our results clearly showed that GPAM influenced cell migration via the signaling lipid lysophosphatidic acid (LPA), linking it with GPAM to cell migration. Analysis of GPAM expression in different cancer types revealed a significant association between high GPAM expression and reduced overall survival in ovarian cancer. Silencing GPAM in ovarian cancer cells decreased cell migration and reduced the growth of tumor xenografts. In contrast to these observations, manipulating CHKA did not influence cell migration in the same set of cell lines. Overall, our findings show how GPAM influences intracellular LPA levels to promote cell migration and tumor growth. Cancer Res; 77(17); 4589-601. ©2017 AACR.

Stem Cell-Derived Immature Human Dorsal Root Ganglia Neurons to Identify Peripheral Neurotoxicants.

UNLABELLED: Safety sciences and the identification of chemical hazards have been seen as one of the most immediate practical applications of human pluripotent stem cell technology. Protocols for the generation of many desirable human cell types have been developed, but optimization of neuronal models for toxicological use has been astonishingly slow, and the wide, clinically important field of peripheral neurotoxicity is still largely unexplored. A two-step protocol to generate large lots of identical peripheral human neuronal precursors was characterized and adapted to the measurement of peripheral neurotoxicity. High content imaging allowed an unbiased assessment of cell morphology and viability. The computational quantification of neurite growth as a functional parameter highly sensitive to disturbances by toxicants was used as an endpoint reflecting specific neurotoxicity. The differentiation of cells toward dorsal root ganglia neurons was tracked in relation to a large background data set based on gene expression microarrays. On this basis, a peripheral neurotoxicity (PeriTox) test was developed as a first toxicological assay that harnesses the potential of human pluripotent stem cells to generate cell types/tissues that are not otherwise available for the prediction of human systemic organ toxicity. Testing of more than 30 chemicals showed that human neurotoxicants and neurite growth enhancers were correctly identified. Various classes of chemotherapeutic agents causing human peripheral neuropathies were identified, and they were missed when tested on human central neurons. The PeriTox test we established shows the potential of human stem cells for clinically relevant safety testing of drugs in use and of new emerging candidates. SIGNIFICANCE: The generation of human cells from pluripotent stem cells has aroused great hopes in biomedical research and safety sciences. Neurotoxicity testing is a particularly important application for stem cell-derived somatic cells, as human neurons are hardly available otherwise. Also, peripheral neurotoxicity has become of major concern in drug development for chemotherapy. The first neurotoxicity test method was established based on human pluripotent stem cell-derived peripheral neurons. The strategies exemplified in the present study of reproducible cell generation, cell function-based test system establishment, and assay validation provide the basis for a drug safety assessment on cells not available otherwise.

Altered Stress-Induced Regulation of Genes in Monocytes in Adults with a History of Childhood Adversity.

Exposure to serious or traumatic events early in life can lead to persistent alterations in physiological stress response systems, including enhanced cross talk between the neuroendocrine and immune system. These programming effects may be mechanistically involved in mediating the effects of adverse childhood experience on disease risk in adulthood. We investigated hormonal and genome-wide mRNA expression responses in monocytes to acute stress exposure, in a sample of healthy adults (n=30) with a history of early childhood adversity, and a control group (n=30) without trauma experience. The early adversity group showed altered hypothalamus-pituitary-adrenal axis responses to stress, evidenced by lower ACTH and cortisol responses. Analyses of gene expression patterns showed that stress-responsive transcripts were enriched for genes involved in cytokine activity, cytokine-cytokine receptor interaction, chemokine activity, and G-protein coupled receptor binding. Differences between groups in stress-induced regulation of gene transcription were observed for genes involved in steroid binding, hormone activity, and G-protein coupled receptor binding. Transcription factor binding motif analysis showed an increased activity of pro-inflammatory upstream signaling in the early adversity group. We also identified transcripts that were differentially correlated with stress-induced cortisol increases between the groups, enriched for genes involved in cytokine-cytokine receptor interaction and glutamate receptor signaling. We suggest that childhood adversity leads to persistent alterations in transcriptional control of stress-responsive pathways, which-when chronically or repeatedly activated-might predispose individuals to stress-related psychopathology.