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Complementing the human genome with an exposome reflects the increasingly obvious impact of environmental exposure, which far exceeds the role of genetics, on human health. Considering the complexity of exposures and, in addition, the reactions of the body to exposures - i.e., the exposome - reverses classical exposure science where the precise measurement of single or few exposures is associated with specific health or environmental effects. The complete description of an individual's exposome is impossible; even less so is that of a population. We can, however, cast a wider net by foregoing some rigor in assessment and compensating with the statistical power of rich datasets. The advent of omics technologies enables a relatively cheap, high-content description of the biological effects of substances, especially in tissues and biofluids. They can be combined with many other rich data-streams, creating big data of exposure and effect. Computational methods increasingly allow data integration, discerning the signal from the noise and formulating hypotheses of exposure-effect relationships. These can be followed up in a targeted way. With a better exposure element in the risk equation, exposomics - new kid on the block of risk assessment - promises to identify novel exposure (interactions) and health/environment effect associations. This may also create opportunities to prioritize the more relevant chemicals for risk assessment, thereby lowering the burden on hazard assessment in an expo-sure-driven approach. Technological developments and synergies between approaches, quality assurance (ultimately as Good Exposome Practices), and the integration of mechanistic thinking will advance this approach. published

Background: Autism spectrum disorder (ASD) is a major public health concern caused by complex genetic and environmental components. Mechanisms of gene–environment (G×E ) interactions and reliable biomarkers associated with ASD are mostly unknown or controversial. Induced pluripotent stem cells (iPSCs) from patients or with clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9)-introduced mutations in candidate ASD genes provide an opportunity to study (G×E) interactions.Objectives: In this study, we aimed to identify a potential synergy between mutation in the high-risk autism gene encoding chromodomain helicase DNA binding protein 8 (CHD8) and environmental exposure to an organophosphate pesticide (chlorpyrifos; CPF) in an iPSC-derived human three-dimensional (3D) brain model.Methods: This study employed human iPSC-derived 3D brain organoids (BrainSpheres) carrying a heterozygote CRISPR/Cas9-introduced inactivating mutation in CHD8 and exposed to CPF or its oxon-metabolite (CPO). Neural differentiation, viability, oxidative stress, and neurite outgrowth were assessed, and levels of main neurotransmitters and selected metabolites were validated against human data on ASD metabolic derangements.Results: Expression of CHD8 protein was significantly lower in CHD8 heterozygous knockout (CHD8+/− ) BrainSpheres compared with CHD8+/+ ones. Exposure to CPF/CPO treatment further reduced CHD8 protein levels, showing the potential (G×E) interaction synergy. A novel approach for validation of the model was chosen: from the literature, we identified a panel of metabolic biomarkers in patients and assessed them by targeted metabolomics in vitro. A synergistic effect was observed on the cholinergic system, S-adenosylmethionine, S-adenosylhomocysteine, lactic acid, tryptophan, kynurenic acid, and α-hydroxyglutaric acid levels. Neurite outgrowth was perturbed by CPF/CPO exposure. Heterozygous knockout of CHD8 in BrainSpheres led to an imbalance of excitatory/inhibitory neurotransmitters and lower levels of dopamine.Discussion: This study pioneered (G×E ) interaction in iPSC-derived organoids. The experimental strategy enables biomonitoring and environmental risk assessment for ASD. Our findings reflected some metabolic perturbations and disruption of neurotransmitter systems involved in ASD. The increased susceptibility of CHD8+/− BrainSpheres to chemical insult establishes a possibly broader role of (G×E) interaction in ASD. published

SUMMARY N-acetyl-aspartyl-glutamate (NAAG) is a peptide-based neurotransmitter that has been extensively studied in many neurological diseases. In this study, we show a specific role of NAAG in cancer. We found that NAAG is more abundant in higher grade cancers and is a source of glutamate in cancers expressing glutamate carboxypeptidase II (GCPII), the enzyme that hydrolyzes NAAG to glutamate and N-acetyl-aspartate (NAA). Knocking down GCPII expression through genetic alteration or pharmacological inhibition of GCPII results in a reduction of both glutamate concentrations and cancer growth. Moreover, targeting GCPII in combination with glutaminase inhibition accentuates these effects. These findings suggest that NAAG serves as an important reservoir to provide glutamate to cancer cells through GCPII when glutamate production from other sources is limited. Thus, GCPII is a viable target for cancer therapy, either alone or in combination with glutaminase inhibition., In Brief Nguyen et al. show that NAAG is more abundant in higher grade cancers and a source of glutamate in cancers expressing GCPII, the enzyme that hydrolyzes NAAG to glutamate and NAA. The results suggest that GCPII is a viable target for cancer therapy, either alone or in combination with glutaminase inhibition., Graphical Abstract

Due to regulatory bans and voluntary substitutions, halogenated polybrominated diphenyl ether (PBDE) flame retardants (FR) are increasingly substituted by mainly organophosphorus FR (OPFR). Leveraging a 3D rat primary neural organotypic in vitro model (rat brainsphere), we compare developmental neurotoxic effects of BDE-47—the most abundant PBDE congener—with four OPFR (isopropylated phenyl phosphate—IPP, triphenyl phosphate—TPHP, isodecyl diphenyl phosphate—IDDP, and tricresyl phosphate (also known as trimethyl phenyl phosphate)—TMPP). Employing mass spectroscopy-based metabolomics and transcriptomics, we observe at similar human-relevant non-cytotoxic concentrations (0.1–5 µM) stronger developmental neurotoxic effects by OPFR. This includes toxicity to neurons in the low µM range; all FR decrease the neurotransmitters glutamate and GABA (except BDE-47 and TPHP). Furthermore, n-acetyl aspartate (NAA), considered a neurologic diagnostic molecule, was decreased by all OPFR. At similar concentrations, the FR currently in use decreased plasma membrane dopamine active transporter expression, while BDE-47 did not. Several findings suggest astrogliosis induced by the OPFR, but not BDE-47. At the 5 µM concentrations, the OPFR more than BDE-47 interfered with myelination. An increase of cytokine gene and receptor expressions suggests that exposure to OPFR may induce an inflammatory response. Pathway/category overrepresentation shows disruption in 1) transmission of action potentials, cell–cell signaling, synaptic transmission, receptor signaling, (2) immune response, inflammation, defense response, (3) cell cycle and (4) lipids metabolism and transportation. Taken together, this appears to be a case of regretful substitution with substances not less developmentally neurotoxic in a primary rat 3D model. Electronic supplementary material The online version of this article (10.1007/s00204-020-02903-2) contains supplementary material, which is available to authorized users.

Introduction: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. Objectives: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC–MS) in untargeted metabolomics. Methods: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. Results: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). Conclusions: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article. Fil: Evans, Anne M.. No especifíca; Fil: ODonovan, Claire. European Bioinformatics Institute; Reino Unido Fil: Playdon, Mary. University of Utah; Estados Unidos Fil: Beecher, Chris. No especifíca; Fil: Beger, Richard D.. National Center For Toxicological Research; Estados Unidos Fil: Bowden, John A.. University of Florida; Estados Unidos Fil: Broadhurst, David. Edith Cowan University; Australia Fil: Clish, Clary B.. Broad Institute; Estados Unidos Fil: Dasari, Surendra. Mayo Clinic; Estados Unidos Fil: Dunn, Warwick B.. University Of Birmingham; Reino Unido Fil: Griffin, Julian L.. University of Cambridge; Estados Unidos. Imperial College London; Reino Unido Fil: Hartung, Thomas. University Johns Hopkins; Estados Unidos Fil: Hsu, Ping Ching. University of Arkansas for Medical Sciences; Estados Unidos Fil: Huan, Tao. University of British Columbia; Canadá Fil: Jans, Judith. University of Utrecht; Países Bajos Fil: Jones, Christina M.. National Institute Of Standards And Technology; Estados Unidos Fil: Kachman, Maureen. Michigan State University; Estados Unidos Fil: Kleensang, Andre. University Johns Hopkins; Estados Unidos Fil: Lewis, Matthew R.. Imperial College London; Reino Unido Fil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina Fil: Mosley, Jonathan D.. Environmental Protection Agency; Estados Unidos Fil: Taylor, Eric. University of Iowa; Estados Unidos Fil: Tayyari, Fariba. University of Iowa; Estados Unidos Fil: Theodoridis, Georgios. Aristotle University; Grecia Fil: Torta, Federico. No especifíca; Fil: Ubhi, Baljit K.. No especifíca; Fil: Vuckovic, Dajana. Concordia University; Canadá

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

The Health Law, Policy & Ethics Project at Emory University School of Law and the Human Toxicology Project Consortium of the Humane Society of the United States co-sponsored a symposium on October 23, 2017, to showcase innovations using human-based in silico and in vitro models for drug and device discovery. The goal of the symposium was to introduce researchers and students to exciting new tools and possible future careers that will increase understanding of disease and improve the search for effective therapeutics, while reducing reliance on animal testing. The symposium concluded with a discussion between scientists and lawyers about the legal regulation of new biomedical research technologies.

To date, most in vitro toxicity testing has focused on acute effects of compounds at high concentrations. This testing strategy does not reflect real-life exposures, which might contribute to long-term disease outcome. We used a 3D-human dopaminergic in vitro LUHMES cell line model to determine whether effects of short-term rotenone exposure (100 nM, 24 h) are permanent or reversible. A decrease in complex I activity, ATP, mitochondrial diameter, and neurite outgrowth were observed acutely. After compound removal, complex I activity was still inhibited; however, ATP levels were increased, cells were electrically active and aggregates restored neurite outgrowth integrity and mitochondrial morphology. We identified significant transcriptomic changes after 24 h which were not present 7 days after wash-out. Our results suggest that testing short-term exposures in vitro may capture many acute effects which cells can overcome, missing adaptive processes, and long-term mechanisms. In addition, to study cellular resilience, cells were re-exposed to rotenone after wash-out and recovery period. Pre-exposed cells maintained higher metabolic activity than controls and presented a different expression pattern in genes previously shown to be altered by rotenone. NEF2L2, ATF4, and EAAC1 were downregulated upon single hit on day 14, but unchanged in pre-exposed aggregates. DAT and CASP3 were only altered after re-exposure to rotenone, while TYMS and MLF1IP were downregulated in both single-exposed and pre-exposed aggregates. In summary, our study shows that a human cell-based 3D model can be used to assess cellular adaptation, resilience, and long-term mechanisms relevant to neurodegenerative research. Electronic supplementary material The online version of this article (10.1007/s00204-018-2250-8) contains supplementary material, which is available to authorized users.

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced " false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets.

Deriving a Pathway of Toxicity from transcriptomic data remains a challenging task. We explore the use of weighted gene correlation network analysis (WGCNA) to extract an initial network from a small microarray study of MPTP toxicity in mice. Five modules were statistically significant; each module was analyzed for gene signatures in the Chemical and Genetic Perturbation subset of the Molecular Signatures Database as well as for over-represented transcription factor binding sites and WGCNA clustered probes by function and captured pathways relevant to neurodegenerative disorders. The resulting network was analyzed for transcription factor candidates, which were narrowed down via text-mining for relevance to the disease model, and then combined with the large-scale interaction FANTOM4 database to generate a genetic regulatory network. Modules were enriched for transcription factors relevant to Parkinson's disease. Transcription factors significantly improved the number of genes that could be connected in a given component. For each module, the transcription factor that had, by far, the highest number of interactions was SP1, and it also had substantial experimental evidence of interactions. This analysis both captures much of the known biology of MPTP toxicity and suggests several candidates for further study. Furthermore, the analysis strongly suggests that SP1 plays a central role in coordinating the cellular response to MPTP toxicity.

Many drugs have progressed through preclinical and clinical trials and have been available - for years in some cases -before being recalled by the FDA for unanticipated toxicity in humans. One reason for such poor translation from drug candidate to successful use is a lack of model systems that accurately recapitulate normal tissue function of human organs and their response to drug compounds. Moreover, tissues in the body do not exist in isolation, but reside in a highly integrated and dynamically interactive environment, in which actions in one tissue can affect other downstream tissues. Few engineered model systems, including the growing variety of organoid and organ-on-a-chip platforms, have so far reflected the interactive nature of the human body. To address this challenge, we have developed an assortment of bioengineered tissue organoids and tissue constructs that are integrated in a closed circulatory perfusion system, facilitating inter-organ responses. We describe a three-tissue organ-on-a-chip system, comprised of liver, heart, and lung, and highlight examples of inter-organ responses to drug administration. We observe drug responses that depend on inter-tissue interaction, illustrating the value of multiple tissue integration for in vitro study of both the efficacy of and side effects associated with candidate drugs. Defense Threat Reduction Agency (DTRA) under Space and Naval Warfare Systems Center Pacific (SSC PACIFIC) [N6601-13-C-2027]; Comprehensive Cancer Center of Wake Forest University NCI CCSG [P30CA012197] The authors gratefully thank Dr. Pedro Baptista for aid in the drug metabolism studies, Steven Forsythe and Meiei Wan for technical aid in maintaining liver and cardiac organoid viability, and Dipasri Konar and Katherine Crowell for technical aid in the lung-on-a-chip operation. The authors gratefully acknowledge funding by the Defense Threat Reduction Agency (DTRA) under Space and Naval Warfare Systems Center Pacific (SSC PACIFIC) Contract No. N6601-13-C-2027. The publication of this material does not constitute approval by the government of the findings or conclusions herein. Proteomics and Metabolomics Core Lab services are supported by the Comprehensive Cancer Center of Wake Forest University NCI CCSG P30CA012197 grant.

As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis. published

Despite the fact that toxicology uses many stand-alone tests, a systematic combination of several information sources very often is required: Examples include: when not all possible outcomes of interest (e.g., modes of action), classes of test substances (applicability domains), or severity classes of effect are covered in a single test; when the positive test result is rare (low prevalence leading to excessive falsepositive results); when the gold standard test is too costly or uses too many animals, creating a need for prioritization by screening. Similarly, tests are combined when the human predictivity of a single test is not satisfactory or when existing data and evidence from various tests will be integrated. Increasingly, kinetic information also will be integrated to make an in vivo extrapolation from in vitro data.Integrated Testing Strategies (ITS) offer the solution to these problems. ITS have been discussed for more than a decade, and some attempts have been made in test guidance for regulations. Despite their obvious potential for revamping regulatory toxicology, however, we still have little guidance on the composition, validation, and adaptation of ITS for different purposes. Similarly, Weight of Evidence and Evidence-based Toxicology approaches require different pieces of evidence and test data to be weighed and combined. ITS also represent the logical way of combining pathway-based tests, as suggested in Toxicology for the 21st Century. This paper describes the state of the art of ITS and makes suggestions as to the definition, systematic combination, and quality assurance of ITS. published

Scientific Reports 6: Article number: 28994; published online: 26 July 2016; updated: 14 September 2016

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

Common recommendations for cell line authentication, annotation and quality control fall short addressing genetic heterogeneity. Within the Human Toxome Project, we demonstrate that there can be marked cellular and phenotypic heterogeneity in a single batch of the human breast adenocarcinoma cell line MCF-7 obtained directly from a cell bank that are invisible with the usual cell authentication by short tandem repeat (STR) markers. STR profiling just fulfills the purpose of authentication testing, which is to detect significant cross-contamination and cell line misidentification. Heterogeneity needs to be examined using additional methods. This heterogeneity can have serious consequences for reproducibility of experiments as shown by morphology, estrogenic growth dose-response, whole genome gene expression and untargeted mass-spectroscopy metabolomics for MCF-7 cells. Using Comparative Genomic Hybridization (CGH), differences were traced back to genetic heterogeneity already in the cells from the original frozen vials from the same ATCC lot, however, STR markers did not differ from ATCC reference for any sample. These findings underscore the need for additional quality assurance in Good Cell Culture Practice and cell characterization, especially using other methods such as CGH to reveal possible genomic heterogeneity and genetic drifts within cell lines.

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value

The use of Integrated Testing Strategies (ITS) permits the combination of diverse types of chemical and toxicological data for the purposes of hazard identification and characterisation. In November 2008, the European Partnership for Alternative Approaches to Animal Testing (EPAA), together with the European Centre for the Validation of Alternative Methods (ECVAM), held a workshop on Overcoming Barriers to Validation of Non-animal Partial Replacement Methods/Integrated Testing Strategies, in Ispra, Italy, to discuss the extent to which current ECVAM approaches to validation can be used to evaluate partial replacement in vitro test methods (i.e. as potential ITS components) and ITS themselves. The main conclusions of these discussions were that formal validation was only considered necessary for regulatory purposes (e.g. the replacement of a test guideline), and that current ECVAM approaches to validation should be adapted to accommodate such test methods (1). With these conclusions in mind, a follow-up EPAA–ECVAM workshop was held in October 2009, to discuss the extent to which existing validation principles are applicable to the validation of ITS test methods, and to develop a draft approach for the validation of such test methods and/or overall ITS for regulatory purposes. This report summarises the workshop discussions that started with a review of the current validation methodologies and the presentation of two case studies (skin sensitisation and acute toxicity), before covering the definition of ITS and their components, including their validation and regulatory acceptance. The following main conclusions/recommendations were made: that the validation of a partial replacement test method (for application as part of a testing strategy) should be differentiated from the validation of an in vitro test method for application as a stand-alone replacement, especially with regard to its predictive capacity; that, in the former case, the predictive capacity of the whole testing strategy (rather than of the individual test methods) would be more important, especially if the individual test methods had a high biological relevance; that ITS allowing for flexible and ad hoc approaches cannot be validated, whereas the validation of clearly defined ITS would be feasible, although practically quite difficult; and that test method developers should be encouraged to develop and submit to ECVAM not only full replacement test methods, but also partial replacement methods to be placed as parts of testing strategies. The added value from the formal validation of testing strategies, and the requirements needed in view of regulatory acceptance of the data, require further informed discussion within the EPAA forum on the basis of case studies provided by industry.

Validation activities of the BALB/c 3T3 cell transformation assay (CTA) – a test method used for the assessment of the carcinogenic potential of compounds – have revealed the need for statistical analysis tailored to specific features of BALB/c 3T3 CTA data. Whereas a standard statistical approach for the Syrian hamster embryo (SHE) CTA was considered sufficient, an international expert group was gathered by the European Centre for the Validation of Alternative Methods (ECVAM) to review commonly applied statistical approaches for BALB/c 3T3 CTA. As it was concluded that none of the commonly applied approaches is entirely appropriate, two novel statistical approaches were found to be recommended for the evaluation of BALB/c 3T3 CTA data accounting for possible non-monotone concentration–response relationship and variance heterogeneity: a negative binomial generalised linear model with William's-type downturn-protected trend tests and a normalisation of the data by a specific transformation allowing for application of a general linear model that estimates effects assuming a normal distribution with William's-type protected tests. Both approaches are described in this article and their performance and the quality of the results they generate is demonstrated using exemplary data. Our work confirmed that both approaches are suitable for the statistical analysis of BALB/c 3T3 CTA data and that each of them is superior to commonly used methods. Furthermore, a procedure dichotomising data into negatives and positives is proposed which allows re-testing in cases where inconclusive data are encountered. The scripts of the statistical evaluation programs written in R – a freely available statistical software – are appended including exemplary outputs ( Appendix A ).

Risk assessment is the process of quantifying the probability of a harmful effect to individuals or populations from human activities. Mechanistic approaches to risk assessment have been generally referred to as systems toxicology. Systems toxicology makes use of advanced analytical and computational tools to integrate classical toxicology and quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Three presentations including two case studies involving both in vitro and in vivo approaches described the current state of systems toxicology and the potential for its future application in chemical risk assessment.

In a genome-wide linkage scan, we aimed at mapping risk loci for dyslexia in the German population. Our sample comprised 1,030 individuals from 246 dyslexia families which were recruited through a single-proband sib pair study design and a detailed assessment of dyslexia and related cognitive traits. We found evidence for a major dyslexia locus on chromosome 6p21. The cognitive trait rapid naming (objects/colors) produced a genome-wide significant LOD score of 5.87 (P = 1.00 × 10−7) and the implicated 6p-risk region spans around 10 Mb. Although our finding maps close to DYX2, where the dyslexia candidate genes DCDC2 and KIAA0319 have already been identified, our data point to the presence of an additional risk gene in this region and are highlighting the impact of 6p21 in dyslexia and related cognitive traits. © 2010 Wiley-Liss, Inc.

Background: The choices of study design and statistical approach for mapping a quantitative trait (QT) are of great importance. Larger sibships and a study design based upon phenotypically extreme siblings can be expected to have a greater statistical power. On the other hand, selected samples and/or deviation from normality can influence the robustness and power. Unfortunately, the effects of violation of multivariate normality assumptions and/or selected samples are only known for a limited number of methods. Some recommendations are available in the literature, but an extensive comparison of robustness and power under several different conditions is lacking. Methods: We compared eight freely available and commonly applied QT mapping methods in a Monte-Carlo simulation study under 36 different models and study designs (three genetic models, three selection schemes, two family structures and the possible effect of deviation from normality). Results: Empirical type I error fractions and empirical power are presented and explained as a whole and for each method separately, followed by a thorough discussion. Conclusions: The results from this extensive comparison could serve as a valuable source for the choice of the study design and the statistical approach for mapping a QT.

Dyslexia is a complex gene-environment disorder with poorly understood etiology that affects about 5% of school-age children. Dyslexia occurs in all languages and is associated with a high level of social and psychological morbidity for the individual and their family; approximately 40-50% have persistent disability into adulthood. The core symptoms are word reading and spelling deficits, but several other cognitive components influence the core phenotype. A broad spectrum of dyslexia related phenotypes, including phonological decoding, phoneme awareness, orthographic processing, short-term memory, rapid naming and basic mathematical abilities, were investigated in large sample of 287 German dyslexia families. We explored the interrelationship between the component phenotypes using correlation and principal component analyses (PCA). In addition, we estimated familiality for phenotypes as well as for the factors suggested by PCA. The correlation between the component phenotypes varied between -0.1 and 0.7. The PCA resulted in three factors: a general dyslexia factor, a speed of processing factor and a mathematical abilities factor. The familiality estimates of single components and factors ranged between 0.25 and 0.63. Instead of analyzing single dyslexia-related components, multivariate analyses including factor analytic approaches may help in the identification of susceptibility genes.

The Human Toxome Project is part of a long-term vision to modernize toxicity testing for the 21st century. In the initial phase of the project, a consortium of six academic, commercial, and government organizations has partnered to map pathways of toxicity, using endocrine disruption as a model hazard. Experimental data is generated at multiple sites, and analyzed using a range of computational tools. While effectively gathering, managing, and analyzing the data for high-content experiments is a challenge in its own right, doing so for a growing number of -omics technologies, with larger data sets, across multiple institutions complicates the process. Interestingly, one of the most difficult, ongoing challenges has been the computational collaboration between the geographically separate institutions. Existing solutions cannot handle the growing heterogeneous data, provide a computational environment for consistent analysis, accommodate different workflows, and adapt to the constantly evolving methods and goals of a research project. To meet the needs of the project, we have created and managed The Human Toxome Collaboratorium, a shared computational environment hosted on third-party cloud services. The Collaboratorium provides a familiar virtual desktop, with a mix of commercial, open-source, and custom-built applications. It shares some of the challenges of traditional information technology, but with unique and unexpected constraints that emerge from the cloud. Here we describe the problems we faced, the current architecture of the solution, an example of its use, the major lessons we learned, and the future potential of the concept. In particular, the Collaboratorium represents a novel distribution method that could increase the reproducibility and reusability of results from similar large, multi-omic studies. published

Dyslexia is characterized as a significant impairment in reading and spelling ability that cannot be explained by low intelligence, low school attendance or deficits in sensory acuity. It is known to be a hereditary disorder that affects about 5% of school aged children, making it the most common of childhood learning disorders. Several susceptibility loci have been reported on chromosomes 1, 2, 3, 6, 15, and 18. The locus on chromosome 18 has been described as having the strongest influence on single word reading, phoneme awareness, and orthographic coding in the largest genome wide linkage study published to date (Fisher et al., 2002). Here we present data from 82 German families in order to investigate linkage of various dyslexia-related traits to the previously described region on chromosome 18p11-q12. Using two- and multipoint analyses, we did not find support for linkage of spelling, single word reading, phoneme awareness, orthographic coding and rapid naming to any of the 14 genotyped STR markers. Possible explanations for our non-replication include differences in study design, limited power of our study and overestimation of the effect of the chromosome 18 locus in the original study.

Objective: Several studies have demonstrated a genetic component for dyslexia. However, both segregation and linkage analyses show contradictory results pointing at the necessity of an optimal ascertainment scheme for molecular genetic studies. Previously, we have argued that the single proband sib pair design (SPSP) would be optimal. The aims of this paper therefore are to demonstrate the practicability of the SPSP design and the estimation of recurrence risks for reading and writing. Methods: We assessed spelling and reading in a family sample ascertained through the SPSP design. 287 families with at least two siblings and their parents were recruited. At least one child was affected with spelling disorder according to a one standard deviation (1SD) discrepancy criterion. Results: Mean values for probands and their siblings were different for both the spelling and the reading phenotype. For the probands, variances of the phenotype spelling were smaller. These effects became stronger with more extreme selection criteria. Both siblings fulfilled the 1SD criterion for spelling and reading in 60.3 and 28.9% of the families, respectively, indicating a low cost efficiency of the double proband sib pair approach. A recurrence risk of 4.52 (CI: 4.07–4.93) was obtained for spelling when the 1SD criterion was applied to both siblings. Recurrence risk estimates were similar for reading. Conclusion: The study demonstrates the suitability of the SPSP design for genetic analysis of dyslexia. The recurrence risk estimates may be used for determining sample sizes in gene mapping studies.

A The OECD validation study of the zebrafish embryo acute toxicity test (ZFET) for acute aquatic toxicity testing evaluated the ZFET reproducibility by testing 20 chemicals at 5 different concentrations in 3 independent runs in at least 3 laboratories. Stock solutions and test concentrations were analytically confirmed for 11 chemicals. Newly fertilised zebrafish eggs (20/concentration and control) were exposed for 96. h to chemicals. Four apical endpoints were recorded daily as indicators of acute lethality: coagulation of the embryo, lack of somite formation, non-detachment of the tail bud from the yolk sac and lack of heartbeat. Results (LC50 values for 48/96. h exposure) show that the ZFET is a robust method with a good intra- and inter-laboratory reproducibility (CV. 30%) for some very toxic or volatile chemicals, and chemicals tested close to their limit of solubility. The ZFET is now available as OECD Test Guideline 236.Considering the high predictive capacity of the ZFET demonstrated by Belanger et al. (2013) in their retrospective analysis of acute fish toxicity and fish embryo acute toxicity data, the ZFET is ready to be considered for acute fish toxicity for regulatory purposes. © 2014 The Authors.

A workshop sponsored by the Human Toxicology Project Consortium (HTPC), "Building Shared Experience to Advance Practical Application of Pathway-Based Toxicology: Liver Toxicity Mode-of-Action" brought together experts from a wide range of perspectives to inform the process of pathway development and to advance two prototype pathways initially developed by the European Commission Joint Research Center (JRC): liver-specific fibrosis and steatosis. The first half of the workshop focused on the theory and practice of pathway development; the second on liver disease and the two prototype pathways. Participants agreed pathway development is extremely useful for organizing information and found that focusing the theoretical discussion on a specific AOP is extremely helpful. In addition, it is important to include several perspectives during pathway development, including information specialists, pathologists, human health and environmental risk assessors, and chemical and product manufacturers, to ensure the biology is well captured and end use is considered.

Toxicity testing typically involves studying adverse health outcomes in animals subjected to high doses of toxicants with subsequent extrapolation to expected human responses at lower doses. The low-throughput of current toxicity testing approaches (which are largely the same for industrial chemicals, pesticides and drugs) has led to a backlog of more than 80,000 chemicals to which human beings are potentially exposed whose potential toxicity remains largely unknown. Employing new testing strategies that employ the use of predictive, high-throughput cell-based assays (of human origin) to evaluate perturbations in key pathways, referred as pathways of toxicity, and to conduct targeted testing against those pathways, we can begin to greatly accelerate our ability to test the vast 'storehouses' of chemical compounds using a rational, risk-based approach to chemi- cal prioritization and provide test results that are more predictive of human toxicity than current methods. The NIH Transforma- tive Research Grant project Mapping the Human Toxome by Systems Toxicology aims at developing the tools for pathway mapping, annotation and validation as well as the respective knowledge base to share this information. The toxic effects of numerous environmental and consumer product chemicals impact on human health. This important fact prompted the National Conversation on Public Health and Chemical Exposures by the US Centers for Disease Con- trol and Prevention (CDC). Two independent studies suggest that we lack the necessary toxicity data for 86% of chemicals currently on the market (1,2). This is a serious public health

The concept of haplotype sharing (HS) has received considerable attention recently, and several haplotype association methods have been proposed. Here, we extend the work of Beckmann and colleagues [Hum Hered 2005; 59:67-78] who derived a HS statistic (BHS) as special case of Mantel’s space-time clustering approach. The Mantel-type HS statistic correlates genetic similarity with phenotypic similarity across pairs of individuals. While phenotypic similarity is measured as the mean-corrected cross product of phenotypes, we propose to incorporate information of the underlying genetic model in the measurement of the genetic similarity. Specifically, for the recessive and dominant modes of inheritance we suggest the use of the minimum and maximum of shared length of haplotypes around a marker locus for pairs of individuals. If the underlying genetic model is unknown, we propose a model-free HS Mantel statistic using the max-test approach. We compare our novel HS statistics to BHS using simulated case-control data and illustrate its use by re-analyzing data from a candidate region of chromosome 18q from the Rheumatoid Arthritis (RA) Consortium. We demonstrate that our approach is point-wise valid and superior to BHS. In the re-analysis of the RA data, we identified three regions with point-wise p-values < 0.005 containing six known genes (PMIP1, MC4R, PIGN, KIAA1468, TNFRSF11A and ZCCHC2) which might be worth follow-up.

Background. Human infections with the tissue nematode Onchocerca volvulus show strong interindividual variation in intensity, which cannot be explained by differences in exposure alone. Several lines of evidence suggest a relevant influence of human genetics. Methods. In a genome-wide search for genetic determinants of resistance, we studied 196 siblings from 51 families exposed to endemic O. volvulus transmission in the forest zone of Ghana, West Africa. The numbers of worm larvae in the skin (i.e., microfilariae), which are the established measure of O. volvulus infection intensity, were counted in 4 small skin biopsy specimens (i.e., skin snips), and the numbers of palpable subcutaneous worm nodules (i.e., onchocercomata) were assessed. Numbers were corrected for age and exposure and were analyzed for linkage to 377 autosomal microsatellite markers and additional markers in genomic regions of interest. Results. Linkage was detected between the numbers of microfilariae and chromosome 2p21-p14 (maximum multipoint log 10 of odds (LOD) score of 3.80 at marker position D2S2378; empirical P = 2.9 X 10 -5 ). Conclusions. This finding provides strong evidence that a human genetic factor influences the intensity of O. volvulus infection. The strength of the linkage signal may facilitate the identification of the decisive genetic variants.

Background: Restless legs syndrome (RLS) is a common sensory-motor disorder characterized by paresthesias and an intense urge to move the legs with a considerable familial aggregation. To date, no gene mutation has been found, but five gene loci have been mapped in primary RLS to chromosomes 12q, 14q, 9p, 2q, and 20p (RLS1 through 5). Patients/Methods: We identified a four-generational German RLS family with 37 family members including 15 affected cases. We performed linkage analysis using microsatellite markers at the five known loci. Prompted by the identification of a potentially shared haplotype near the RLS3 locus, we expanded the investigated linkage region on chromosome 9p using additional DNA markers. Results: Mode of inheritance in our RLS family was compatible with an autosomal dominant pattern, and disease onset was mainly in childhood or adolescence. We excluded linkage to the RLS1, RLS2, RLS4, and RLS5 loci. However, we identified a likely new RLS gene locus (RLS3*) on chromosome 9p with a maximum lod score of 3.60 generated by model-based multipoint linkage analysis. A haplotype flanked by D9S974 and D9S1118 in a 9.9-Mb region, centromeric to RLS3, was shared by all 12 investigated patients. In addition, 11 of them carried a common haplotype extending telomeric to D9S2189 that is located within RLS3. Conclusions: We demonstrate linkage to a locus on chromosome 9p that is probably distinct from RLS3. Our family with a rather homogeneous phenotype and very early disease onset represents a unique opportunity to further elucidate the genetic causes of the frequent restless leg syndrome.

Although balancing selection with the sickle-cell trait and other red blood cell disorders has emphasized the interaction between malaria and human genetics, no systematic approach has so far been undertaken towards a comprehensive search for human genome variants influencing malaria. By screening 2,551 families in rural Ghana, West Africa, 108 nuclear families were identified who were exposed to hyperendemic malaria transmission and were homozygous wild-type for the established malaria resistance factors of hemoglobin (Hb)S, HbC, alpha+ thalassemia, and glucose-6-phosphate-dehydrogenase deficiency. Of these families, 392 siblings aged 0.5–11 y were characterized for malaria susceptibility by closely monitoring parasite counts, malaria fever episodes, and anemia over 8 mo. An autosome-wide linkage analysis based on 10,000 single-nucleotide polymorphisms was conducted in 68 selected families including 241 siblings forming 330 sib pairs. Several regions were identified which showed evidence for linkage to the parasitological and clinical phenotypes studied, among them a prominent signal on Chromosome 10p15 obtained with malaria fever episodes (asymptotic z score = 4.37, empirical p-value = 4.0 × 10−5, locus-specific heritability of 37.7%; 95% confidence interval, 15.7%–59.7%). The identification of genetic variants underlying the linkage signals may reveal as yet unrecognized pathways influencing human resistance to malaria., Author Summary In tropical Africa, virtually all children become infected with malaria parasites. Most of them experience several malaria attacks per year, and over a million die from disease complications. Sickle-cell anemia, thalassemias, and other inherited red blood cell disorders indicate that malaria has selected for human genetic variants, but no attempts have so far been reported to systematically screen the human genome for malaria-resistance factors. We describe a genome-wide linkage analysis performed in children living in rural Ghana, West Africa, including approaches to select an informative study cohort and to assess, over a period of 8 mo, individual disposition to malaria parasitemia, fever episodes, and anemia. Families carrying the known malaria-protective red blood cell disorders were excluded, infection intensities were adjusted to the use of mosquito-protection devices, and parasitological and clinical findings were corrected according to the state of partial malaria immunity, which, under constant exposure, gradually develops over the first 10 y of life. The study revealed several genomic regions showing evidence for linkage to the various malaria phenotypes recorded, among them a prominent signal on Chromosome 10 correlated to the frequency of fever episodes. Future identification of genes involved is expected to reveal previously unrecognized pathways that may protect children against malaria.

The new European Union REACH (Registration, Evaluation and Authorisation of new and existing CHemicals) policy will increase the use of laboratory animals if alternative methods are not available. The immune system is a target for many chemicals and drugs, with potential adverse effects on human health. We performed an inter-laboratory pre-validation of a set of in vitro assays to detect immunosuppressive capacity. Human PBMC and murine and rat splenocytes were used. Six immunosuppressive compounds (benzo(a)pyrene, cyclophosphamide, methotrexate, rapamycin, dexamethasone, and urethane) and one non-immunosuppressive compound (D-mannitol) were tested. Basal cytotoxicity was assessed by measuring LDH-release. Human PBMC were stimulated with anti-CD3/anti-CD28, murine splenocytes with anti-CD3/anti-CD28, Con A, LPS, and anti-CD40/IL-4, and rat splenocytes with Con A and LPS. Cell proliferation and IFN-γ and TNF-α production were measured. Five out of 6 immunosuppressive compounds could be identified as such, and the non-immunotoxic one came out as negative. This suggests that in vitro assays are able to detect immunosuppressive capacity. The sensitivity of the various species/stimulant/endpoint combinations ranged between 38 and 61%, while the specificity ranged between 67 and 100%. This suggests that from these results a choice for specific species/stimulant/endpoint combinations cannot be made.

Seven years after the last release, the European Commission has again collated and released data on laboratory animal use. The new report is the first to correspond to the requirements of the new Directive 2010/63/EU. Beside minor problems in reporting, the new reporting format is a major step forward, with additional new categories like severity allowing insight into animal use related questions that goes far beyond the previous reports. An in-depth analysis confirms a slight decrease in animal use from 2015 to 2017, but also compared to the 2005, 2008 and 2011 reports, though the new reporting scheme makes this comparison difficult. Notable success is evident for replacing rabbit pyrogen testing but, in general, the implementation of accepted alternative methods lags behind expec-tations. Beside the roughly 10 million animals per year covered in the report, about 8 million animals were identified that fall under the Directive but are not included in this number. Their omission downplays the impact of REACH on animal use. The report, second to none in its detail internationally, represents an important instrument for benchmarking and strategi-cally focusing activities in the 3Rs. published

In the search for Onchocerca volvulus antigens possibly involved in protection against human onchocerciasis, partial amino acid sequence analysis of one of the O. volvulus antigens of the serologically identified proteins showed a close relationship to the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein family. Subsequent adult worm cDNA library screening and cloning produced a clone of 1650 bp. An open reading frame spans over 1020 bp encoding for a protein of 340 amino acids with an apparent molecular weight of 38000. Comparison of the complete amino acid sequence identified this protein as a member of the GAPDH protein family. The recombinantly expressed protein shows GAPDH enzymatic activity as well as plasminogen-binding capacity. DNA sequence analysis of the corresponding gene revealed the presence of two introns. Using immunohistology Ov-GAPDH was observed in microfilariae, infective larvae, and adult male and female worms. Most striking was the labelling of the musculature of the body wall. Labelling was also observed in the pseudocoeloma cavity and in a subset of cell nuclei, suggesting additional, non-glycolytic functions of the Ov-GAPDH. Gene gun immunization with the DNA-construct in cattle led to specific humoral immune responses. Thus, the protective potential of the DNA-construct of Ov-GAPDH can be evaluated in vaccination trials using animal models such as the cattle/Onchocerca ochengi model.

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Haplotype-based methods have become increasingly popular in the last decade because shared lengths in haplotypes can be used for disease localization. In this contribution, we propose a novel linkage-based haplotype-sharing approach for quantitative traits based on the class of Mantel statistics which is closely related to the weighted pair-wise correlation statistic. Because these statistics are known to be liberal, we propose a permutation test to evaluate significance. We applied the Mantel statistic to the autosomal data from the genome-wide scan of the Collaborative Study on the Genetics of Alcoholism with the Affymetrix Genotype 10 K array that was provided for the Genetic Analysis Workshop 14. Four regions on chromosome 4, 8, 16, and 20 showed p-values less than 0.005 with a minimum p-value of < 0.0001 on chromosome 16 (tsc0520638 at 72.8 cM). Three of these four regions located on chromosome 4, 16, and 20 have been reported previously in the Genetic Analysis Workshop 11.