CAGm: a repository of germline microsatellite variations in the 1000 genomes project.

The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): a database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: a mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/.

Assessment of zebrafish embryo photomotor response sensitivity and phase-specific patterns following acute- and long-duration exposure to neurotoxic chemicals and chemical weapon precursors.

Zebrafish are an attractive model for chemical screening due to their adaptability to high-throughput platforms and ability to display complex phenotypes in response to chemical exposure. The photomotor response (PMR) is an established and reproducible phenotype of the zebrafish embryo, observed 24 h post-fertilization in response to a predefined sequence of light stimuli. In an effort to evaluate the sensitivity and effectiveness of the zebrafish embryo PMR assay for toxicity screening, we analyzed chemicals known to cause both neurological effects and developmental abnormalities, following both short (1 h) and long (16 h+) duration exposures. These include chemicals that inhibit aerobic respiration (eg, cyanide), acetyl cholinesterase inhibitors (organophosphates pesticides) and several chemical weapon precursor compounds with variable toxicity profiles and poorly understood mechanisms of toxicity. We observed notable concentration-responsive, phase-specific effects in the PMR after exposure to chemicals with a known mechanism of action. Chemicals with a more general toxicity profile (toxic chemical weapon precursors) appeared to reduce all phases of the PMR without a notable phase-specific effect. Overall, 10 of 20 chemicals evaluated elicited an effect on the PMR response and eight of those 10 chemicals were picked up in both the short- and long-duration assays. In addition, the patterns of response uniquely differentiated chemical weapon precursor effects from those elicited by inhibitors of aerobic respiration and organophosphates. By providing a rapid screening test for neurobehavioral effects, the zebrafish PMR test could help identify potential mechanisms of action and target compounds for more detailed follow-on toxicological evaluations. Approved for public release: distribution unlimited.

Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure-Activity Relationship Models Based on Deep Neural Networks?

Deep neural networks (DNNs) are the major drivers of recent progress in artificial intelligence. They have emerged as the machine-learning method of choice in solving image and speech recognition problems, and their potential has raised the expectation of similar breakthroughs in other fields of study. In this work, we compared three machine-learning methods-DNN, random forest (a popular conventional method), and variable nearest neighbor (arguably the simplest method)-in their ability to predict the molecular activities of 21 in vivo and in vitro data sets. Surprisingly, the overall performance of the three methods was similar. For molecules with structurally close near neighbors in the training sets, all methods gave reliable predictions, whereas for molecules increasingly dissimilar to the training molecules, all three methods gave progressively poorer predictions. For molecules sharing little to no structural similarity with the training molecules, all three methods gave a nearly constant value-approximately the average activity of all training molecules-as their predictions. The results confirm conclusions deduced from analyzing molecular applicability domains for accurate predictions, i.e., the most important determinant of the accuracy of predicting a molecule is its similarity to the training samples. This highlights the fact that even in the age of deep learning, developing a truly high-quality model relies less on the choice of machine-learning approach and more on the availability of experimental efforts to generate sufficient training data of structurally diverse compounds. The results also indicate that the distance to training molecules offers a natural and intuitive basis for defining applicability domains to flag reliable and unreliable quantitative structure-activity relationship predictions.

General Approach to Estimate Error Bars for Quantitative Structure-Activity Relationship Predictions of Molecular Activity.

Key requirements for quantitative structure-activity relationship (QSAR) models to gain acceptance by regulatory authorities include a defined domain of applicability (DA) and appropriate measures of goodness-of-fit, robustness, and predictivity. Hence, many DA metrics have been developed over the past two decades. The most intuitive are perhaps distance-to-model metrics, which are most commonly defined in terms of the mean distance between a molecule and its k nearest training samples. Detailed evaluations have shown that the variance of predictions by an ensemble of QSAR models may serve as a DA metric and can outperform distance-to-model metrics. Intriguingly, the performance of ensemble variance metric has led researchers to conclude that the error of predicting a new molecule does not depend on the input descriptors or machine-learning methods but on its distance to the training molecules. This implies that the distance to training samples may serve as the basis for developing a high-performance DA metric. In this article, we introduce a new Tanimoto distance-based DA metric called the sum of distance-weighted contributions (SDC), which takes into account contributions from all molecules in a training set. Using four acute chemical toxicity data sets of varying sizes and four other molecular property data sets, we demonstrate that SDC correlates well with the prediction error for all data sets regardless of the machine-learning methods and molecular descriptors used to build the QSAR models. Using the acute toxicity data sets, we compared the distribution of prediction errors with respect to SDC, the mean distance to k-nearest training samples, and the variance of random forest predictions. The results showed that the correlation with the prediction error was highest for SDC. We also demonstrate that SDC allows for the development of robust root mean squared error (RMSE) models and makes it possible to not only give a QSAR prediction but also provide an individual RMSE estimate for each molecule. Because SDC does not depend on a specific machine-learning method, it represents a canonical measure that can be widely used to estimate individual molecule prediction errors for any machine-learning method.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

A novel approach to interrogating the effects of chemical warfare agent exposure using organ-on-a-chip technology and multiomic analysis.

Organ-on-a-chip platforms are utilized in global bioanalytical and toxicological studies as a way to reduce materials and increase throughput as compared to in vivo based experiments. These platforms bridge the infrastructure and regulatory gaps between in vivo animal work and human systems, with models that exemplify active biological pathways. In conjunction with the advent of increased capabilities associated with next generation sequencing and mass spectrometry based '-omic' technologies, organ-on-a-chip platforms provide an excellent opportunity to investigate the global changes at multiple biological levels, including the transcriptome, proteome and metabolome. When investigated concurrently, a complete profile of cellular and regulatory perturbations can be characterized following treatment with specific agonists. In this study, global effects were observed and analyzed following liver chip exposure to the chemical warfare agent, VX. Even though the primary mechanism of action of VX (i.e. acetylcholinesterase inhibition) is well characterized, recent in vivo studies suggest additional protein binding partners that are implicated in metabolism and cellular energetic pathways. In addition, secondary toxicity associated with peripheral organ systems, especially in human tissues, is not well defined. Our results demonstrate the potential of utilizing an organ-on-a-chip platform as a surrogate system to traditional in vivo studies. This is realized by specifically indicating significant dysregulation of several cellular processes in response to VX exposure including but not limited to amino acid synthesis, drug metabolism, and energetics pathways.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Mind the gaps: Prioritizing activities to meet regulatory needs for acute systemic lethality

Efforts are underway to develop and implement nonanimal approaches which can characterize acute systemic lethality. A workshop was held in October 2019 to discuss developments in the prediction of acute oral lethality for chemicals and mixtures, as well as progress and needs in the understanding and modeling of mechanisms of acute lethality. During the workshop, each speaker led the group through a series of charge questions to determine clear next steps to progress the aims of the workshop. Participants concluded that a variety of approaches will be needed and should be applied in a tiered fashion. Non-testing approaches, including waiving tests, computational models for single chemicals, and calculating the acute lethality of mixtures based on the LD50 values of mixture components, could be used for some assessments now, especially in the very toxic or non-toxic classification ranges. Agencies can develop policies indicating contexts under which mathematical approaches for mixtures assessment are acceptable; to expand applicability, poorly predicted mixtures should be examined to understand discrepancies and adapt the approach. Transparency and an understanding of the variability of in vivo approaches are crucial to facilitate regulatory application of new approaches. In a replacement strategy, mechanistically based in vitro or in silico models will be needed to support non-testing approaches especially for highly acutely toxic chemicals. The workshop discussed approaches that can be used in the immediate or near term for some applications and identified remaining actions needed to implement approaches to fully replace the use of animals for acute systemic toxicity testing.

Changing the dose metric for inhalation toxicity studies: short-term study in rats with engineered aerosolized amorphous silica nanoparticles.

Inhalation toxicity and exposure assessment studies for nonfibrous particulates have traditionally been conducted using particle mass measurements as the preferred dose metric (i.e., mg or microg/m(3)). However, currently there is a debate regarding the appropriate dose metric for nanoparticle exposure assessment studies in the workplace. The objectives of this study were to characterize aerosol exposures and toxicity in rats of freshly generated amorphous silica (AS) nanoparticles using particle number dose metrics (3.7 x 10(7) or 1.8 x 10(8) particles/cm(3)) for 1- or 3-day exposures. In addition, the role of particle size (d(50) = 37 or 83 nm) on pulmonary toxicity and genotoxicity endpoints was assessed at several postexposure time points. A nanoparticle reactor capable of producing, de novo synthesized, aerosolized amorphous silica nanoparticles for inhalation toxicity studies was developed for this study. SiO(2) aerosol nanoparticle synthesis occurred via thermal decomposition of tetraethylorthosilicate (TEOS). The reactor was designed to produce aerosolized nanoparticles at two different particle size ranges, namely d(50) = approximately 30 nm and d(50) = approximately 80 nm; at particle concentrations ranging from 10(7) to 10(8) particles/cm(3). AS particle aerosol concentrations were consistently generated by the reactor. One- or 3-day aerosol exposures produced no significant pulmonary inflammatory, genotoxic, or adverse lung histopathological effects in rats exposed to very high particle numbers corresponding to a range of mass concentrations (1.8 or 86 mg/m(3)). Although the present study was a short-term effort, the methodology described herein can be utilized for longer-term inhalation toxicity studies in rats such as 28-day or 90-day studies. The expansion of the concept to subchronic studies is practical, due, in part, to the consistency of the nanoparticle generation method.

An Integrated Multi-Omics Analysis Defines Key Pathway Alterations in a Diet-Induced Obesity Mouse Model.

Obesity is a multifactorial disease with many complications and related diseases and has become a global epidemic. To thoroughly understand the impact of obesity on whole organism homeostasis, it is helpful to utilize a systems biological approach combining gene expression and metabolomics across tissues and biofluids together with metagenomics of gut microbial diversity. Here, we present a multi-omics study on liver, muscle, adipose tissue, urine, plasma, and feces on mice fed a high-fat diet (HFD). Gene expression analyses showed alterations in genes related to lipid and energy metabolism and inflammation in liver and adipose tissue. The integration of metabolomics data across tissues and biofluids identified major differences in liver TCA cycle, where malate, succinate and oxaloacetate were found to be increased in HFD mice. This finding was supported by gene expression analysis of TCA-related enzymes in liver, where expression of malate dehydrogenase was found to be decreased. Investigations of the microbiome showed enrichment of Lachnospiraceae, Ruminococcaceae, Streptococcaceae and Lactobacillaceae in the HFD group. Our findings help elucidate how the whole organism metabolome and transcriptome are integrated and regulated during obesity.

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30 000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity.

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substance's physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances.

Synergistic Gene Expression Signature Observed in TK6 Cells upon Co-Exposure to UVC-Irradiation and Protein Kinase C-Activating Tumor Promoters.

Activation of stress response pathways in the tumor microenvironment can promote the development of cancer. However, little is known about the synergistic tumor promoting effects of stress response pathways simultaneously induced in the tumor microenvironment. Therefore, the purpose of this study was to establish gene expression signatures representing the interaction of pathways deregulated by tumor promoting agents and pathways induced by DNA damage. Human lymphoblastoid TK6 cells were pretreated with the protein kinase C activating tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA) and exposed to UVC-irradiation. The time and dose-responsive effects of the co-treatment were captured with RNA-sequencing (RNA-seq) in two separate experiments. TK6 cells exposed to both TPA and UVC had significantly more genes differentially regulated than the theoretical sum of genes induced by either stress alone, thus indicating a synergistic effect on global gene expression patterns. Further analysis revealed that TPA+UVC co-exposure caused synergistic perturbation of specific genes associated with p53, AP-1 and inflammatory pathways important in carcinogenesis. The 17 gene signature derived from this model was confirmed with other PKC-activating tumor promoters including phorbol-12,13-dibutyrate, sapintoxin D, mezerein, (-)-Indolactam V and resiniferonol 9,13,14-ortho-phenylacetate (ROPA) with quantitative real-time PCR (QPCR). Here we show a novel gene signature that may represent a synergistic interaction in the tumor microenvironment that is relevant to the mechanisms of chemical induced tumor promotion.

Protein kinase C-activating tumor promoters modulate the DNA damage response in UVC-irradiated TK6 cells.

12-O-Tetradecanoylphorbol-13-acetate (TPA) is a non-genotoxic tumor promoter that dysregulates the protein kinase C (PKC) pathway and causes variable cellular responses to DNA damage in different experimental models. In the present study, we pretreated human lymphoblastoid TK6 cells (wild-type p53) for 72 h with TPA, and five other PKC-activating tumor promoters, to determine how sustained exposure to these chemicals modulates key DNA damage response (DDR) endpoints induced by UVC-irradiation. Here we show that pre-treatment with PKC-activating tumor promoters augmented the sensitivity of TK6 cells to UVC-irradiation characterized by a synergistic increase in apoptosis compared to that induced by either stress alone. In addition, high residual levels of the DNA damage repair signal γH2AX was observed in tumor promoter treated cells indicating a delayed DDR recovery. NH32 (p53-null, isogenic to TK6) cells were resistant to the synergistic effects on apoptosis implicating p53 as a central mediator of the DDR modulating effects. In addition, analysis of p53 target genes in TPA-pre-treated TK6 cells revealed a significant modulation of UVC-induced gene expression that supported a shift toward a pro-apoptotic phenotype. Therefore, sustained exposure to tumor promoting agents modulates the UVC-induced DDR in TK6 cells, which may represent important synergistic interactions that occur during tumor promotion.

Characterization of cellular uptake of perfluorooctanoate via organic anion-transporting polypeptide 1A2, organic anion transporter 4, and urate transporter 1 for their potential roles in mediating human renal reabsorption of perfluorocarboxylates.

It has been hypothesized that human renal apical membrane transporters play a key role in human renal reabsorption of perfluorooctanoate (PFO), which contributes to the long half-life of PFO in humans. In the present study, PFO uptake kinetics of human organic anion-transporting polypeptide (OATP) 1A2, organic anion transporter (OAT) 4, and urate transporter 1 (URAT1) in stably transfected cell lines was investigated. OAT4 and URAT1, but not OATP1A2, were shown to mediate saturable PFO cellular uptake. OAT4-mediated PFO uptake was stimulated by a low extracellular pH, which was evidenced as a lower Michaelis constant (K(m)) at pH 6 (172.3 ± 45.9µM) than that at pH 7.4 (310.3 ± 30.2µM). URAT1-mediated PFO uptake was greatly enhanced by an outward Cl(-) gradient, and its K(m) value was determined to be 64.1 ± 30.5µM in the absence of extracellular Cl(-). The inhibition of OATP1A2- or OAT4-mediated estrone-3-sulfate uptake or URAT1-mediated urate uptake has been compared for linear perfluorocarboxylates (PFCs) with carbon chain lengths from 4 to 12. A clear chain length-dependent inhibition was observed, suggesting that PFCs in general are substrates of OAT4 and URAT1 but with different levels of affinities to the transporters depending on their chain length. Our results suggest that OAT4 and URAT1 are key transporters in renal reabsorption of PFCs in humans and, as a result, may contribute significantly to the long half-life of PFO in humans.

Cryopreserved hepatocytes from rainbow trout (Oncorhynchus mykiss): a validation study to support their application in bioaccumulation assessment.

Determination of biotransformation rates of xenobiotics in freshly isolated trout hepatocytes has been demonstrated to significantly improve the performance of bioaccumulation assessment models. In order to promote this in vitro approach, trout hepatocytes need to be cryopreserved to facilitate their availability while ensuring their metabolic competency. In the present study, we obtained basal level metabolic enzyme activities for cytochrome P450 (CYP) 1A, CYP3A, glutathione-S-transferase, and uridine 5'-diphospho-glucuronosyltransferase from trout hepatocytes cryopreserved for various periods of time up to three months and compared their values with those obtained from freshly isolated hepatocytes. Similarly, we compared intrinsic clearance (CL(int)) values determined in cryopreserved trout hepatocytes to those determined in freshly isolated hepatocytes for reference compounds molinate, michler's ketone, 4-nonylphenol, 2,4-ditert-butylphenol, benzo(a)pyrene, and pyrene. Our results show that cryopreserved trout hepatocytes maintained greater than 75% of their basal level enzyme activities and greater than 72% of xenobiotic biotransformation capabilities, regardless of the length of cryostorage. As a result, bioconcentration factors of the reference compounds were adequately predicted based on the CL(int) values. We simulated the condition for shipping cryopreserved trout hepatocytes and demonstrated that 24 h dry ice storage did not negatively affect the rates of xenobiotic biotransformation. We conclude that cryopreserved trout hepatocytes are suitable for biotransformation rate determination of xenobiotics in vitro, and therefore, are an acceptable alternative to freshly isolated trout hepatocytes in the application in bioaccumulation assessment.

Organic anion transporting polypeptide (Oatp) 1a1-mediated perfluorooctanoate transport and evidence for a renal reabsorption mechanism of Oatp1a1 in renal elimination of perfluorocarboxylates in rats.

Organic anion transporting polypeptide (Oatp) 1a1 has been hypothesized to play a key role in rat renal reabsorption of perfluorooctanoate (PFO). We have investigated PFO uptake kinetics in Chinese Hamster Ovary (CHO) cells that have been stably transfected with the cDNA encoding Oatp1a1. The Oatp1a1-expressing CHO cells have been validated by their Oatp1a1 gene expression, estrone-3-sulfate (E3S) uptake kinetics, and the correlation between Oatp1a1 gene expression and E3S uptake activity that were both induced by the treatment of sodium butyrate. Oatp1a1-mediated PFO uptake underwent a saturable process with a K(m) value of 162.2+/-20.2microM, which was effectively inhibited by known Oatp1a1 substrates sulfobromophthalein and taurocholate, and a major flavonoid in grapefruit juice, naringin. The inhibition of Oatp1a1-mediated E3S uptake has been compared for linear perfluorocarboxylates with carbon chain lengths ranged from 4 to 12. There was no apparent inhibition by perfluorobutanoate and perfluoropentanoate at 1mM. Inhibition was observed for perfluorohexanoate at 1mM and the level of inhibition increased as the increase of the chain length up to perfluorodecanoate. The values of apparent inhibition constant (K(i,app)) were determined for perfluorocarboxylates with chain lengths between 6 and 10. The log values of K(i,app) exhibited a negative linear relationship to the chain lengths and a positive linear relationship to the log values of the total clearance of perfluorocarboxylates in male rats. This in vitro-to-in vivo correlation strongly supports a tubular reabsorptive role of Oatp1a1 in rat renal elimination of perfluorocarboxylates. Due to the sex-dependent expression of Oatp1a1 in rat kidney, Oatp1a1-mediated tubular reabsorption is suggested to be the mechanism for the sex-dependent renal elimination of PFO in rats.