Molecular Networks of Platinum Drugs and Their Interaction with microRNAs in Cancer

The precise mechanism of resistance to anti-cancer drugs such as platinum drugs is not fully revealed. To reveal the mechanism of drug resistance, the molecular networks of anti-cancer drugs such as cisplatin, carboplatin, oxaliplatin, and arsenic trioxide were analyzed in several types of cancers. Since diffuse-type stomach adenocarcinoma, which has epithelial-mesenchymal transition (EMT)-like characteristics, is more malignant than intestinal-type stomach adenocarcinoma, the gene expression and molecular networks in diffuse- and intestinal-type stomach adenocarcinomas were analyzed. Analysis of carboplatin revealed the causal network in diffuse large B-cell lymphoma. The upstream regulators of the molecular networks of cisplatin-treated lung adenocarcinoma included the anti-cancer drug trichostatin A (TSA), a histone deacetylase inhibitor. The upstream regulator analysis of cisplatin revealed an increase in FAS, BTG2, SESN1, and CDKN1A, and the involvement of the tumor microenvironment pathway. The molecular networks were predicted to interact with several microRNAs, which may contribute to the identification of new drug targets for drug-resistant cancer. Analysis of oxaliplatin, a platinum drug, revealed that the SPINK1 pancreatic cancer pathway is inactivated in ischemic cardiomyopathy. The study showed the importance of the molecular networks of anti-cancer drugs and tumor microenvironment in the treatment of cancer resistant to anti-cancer drugs.

Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine)

The nitramine explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is associated with acute and chronic toxicity in mammals and targets both the central nervous system and liver. After a single oral dose of RDX in male rats, the systemic distribution of RDX and the toxicodynamic response was measured using clinical chemistry and Affymetrix Rat Genome® 230 2.0 gene expression arrays, respectively. Nominal doses of 0, 9 and 36 mg/kg pure RDX were administered to animals followed by liver, cerebral cortex, and hippocampus gene expression analysis at 0, 3.5, 24, and 48 hours. RDX quickly entered the liver and brain, increasing up to 24 hours. For the 36 mg/kg dose, RDX was still measurable in liver and brain at 48 hours, but was non-detectible for the 9 mg/kg dose. At 3.5 hours, the time within which most convulsions reportedly occur after RDX ingestion, the hippocampus displayed the highest response for both gene expression and pathways, while the cortex was relatively non-responsive. The top 2 impacted pathways, primarily involved in neurotransmission, were the GABAergic and glutamatergic pathways. High numbers of genes also responded to RDX in the liver with P450 metabolism pathways significantly involved. Compared to the liver, the hippocampus displayed more consistent biological effects across dose and time with neurotransmission pathways predominating. Overall, based on gene expression data, RDX responses were high in both the hippocampus and liver, but were minimal in the cerebral cortex. These results identify the hippocampus as an important target for RDX based on gene expression.

Male fathead minnow transcriptomes and associated chemical analytes in the Milwaukee estuary system

Contaminants of Emerging Concern (CECs) can be measured in waters across the United States, including the tributaries of the Great Lakes. The extent to which these contaminants affect gene expression in aquatic wildlife is unclear. This dataset presents the full hepatic transcriptomes of laboratory-reared fathead minnows (Pimephales promelas) caged at multiple sites within the Milwaukee Estuary Area of Concern and control sites. Following 4 days of in situ exposure, liver tissue was removed from males at each site for RNA extraction and sequencing, yielding a total of 116 samples from which libraries were prepared, pooled, and sequenced. For each exposure site, 179 chemical analytes were also assessed. These data were created with the intention of inviting research on possible transcriptomic changes observed in aquatic species exposed to CECs. Access to both full sequencing reads of animal samples as well as water contaminant data across multiple Great Lakes sites will allow others to explore the health of these ecosystems in support of the aims of the Great Lakes Restoration Initiative.

Measurement(s)	transcripts • water chemistry
Technology Type(s)	RNAseq • GC/MS
Factor Type(s)	exposure
Sample Characteristic - Organism	Pimephales promelas
Sample Characteristic - Environment	estuary system
Sample Characteristic - Location	Milwaukee, WI, USA

Current ecotoxicity testing needs among selected U.S. federal agencies

U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies' regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations.

Developmental, Behavioral and Transcriptomic Changes in Zebrafish Embryos after Smoke Dye Exposure

(1) Background: Disperse Blue 14, Disperse Red 9, Solvent Red 169 and Solvent Yellow 33 have been used to color smoke; however, they have not been comprehensively assessed for their potential health hazards. (2) Methods: To assess the effects of these dyes, zebrafish embryos were exposed from 6 to 120 h post fertilization (hpf) to 10-55 µM Disperse Red 9, 1-50 µM Solvent Red 169, 7.5-13.5 µM Solvent Yellow 33 or 133-314 µM Disperse Blue 14. Embryos were monitored for adverse effects on gene expression at 48 hpf as well as for mortality, development and behavior at 120 hpf. The dyes were examined for their potential to cross the blood-brain barrier. (3) Results: Solvent Yellow 33 and Disperse Blue 14 impaired development and behavior at all concentrations. Disperse Red 9 impaired behavior at all concentrations and development at all concentrations except for 10 µM. Solvent Red 169 caused no effects. Mortality was only seen in Disperse Blue 14 at 261.5 and 314 µM. Gene expression indicated impacts on neurodevelopment and folate and retinol metabolism as potential mechanisms of toxicity. (4) Conclusions: Smoke dyes have a high potential for causing developmental changes and neurotoxicity and should be examined more closely using comprehensive approaches as used here.

Integration of Adverse Outcome Pathways, Causal Networks and ‘Omics to Support Chemical Hazard Assessment

Several approaches have been used in an attempt to simplify and codify the events that lead to adverse effects of chemicals including systems biology, ‘omics, in vitro assays and frameworks such as the Adverse Outcome Pathway (AOP). However, these approaches are generally not integrated despite their complementary nature. Here we propose to integrate toxicogenomics data, systems biology information and AOPs using causal biological networks to define Key Events in AOPs. We demonstrate this by developing a causal subnetwork of 28 nodes that represents the Key Event of regenerative proliferation – a critical event in AOPs for liver cancer. We then assessed the effects of three chemicals known to cause liver injury and cell proliferation (carbon tetrachloride, aflatoxin B₁, thioacetamide) and two with no known cell proliferation effects (diazepam, simvastatin) on the subnetwork using rat liver gene expression data from the toxicogenomic database Open TG-GATEs. Cyclin D1 (Ccnd1), a gene both causally linked to and sufficient to infer regenerative proliferation activity, was overexpressed after exposures to carbon tetrachloride, aflatoxin B₁ and thioacetamide, but not in exposures to diazepam and simvastatin. These results were consistent with known effects on rat livers and liver pathology of exposed rats. Using these approaches, we demonstrate that transcriptomics, AOPs and systems biology can be applied to examine the presence and progression of AOPs in order to better understand the hazards of chemical exposure.

Translatable pathways classification (TransPath-C) for inferring processes germane to human biology from animal studies data: example application in neurobiology

How to translate insights gained from studies in one organismal species for what is most likely to be germane in another species, such as from mice to humans, is a ubiquitous challenge in basic biology as well as biomedicine. This is an especially difficult problem when there are few molecular features that are obviously important in both species for a given phenotype of interest. Neuropathologies are a prominent realm of this complication. Schizophrenia is complex psychiatric disorder that affects 1% of the population. Many genetic factors have been proposed to drive the development of schizophrenia, and the 22q11 microdeletion (MD) syndrome has been shown to dramatically increase this risk. Due to heterogeneity of presentation of symptoms, diagnosis and formulation of treatment options for patients can often be delayed, and there is an urgent need for novel therapeutics directed toward the treatment of schizophrenia. Here, we present a novel computational approach, Translational Pathways Classification (TransPath-C), that can be used to identify shared pathway dysregulation between mouse models and human schizophrenia cohorts. This method uses variation of pathway activation in the mouse model to predict both mouse and human disease phenotype. Analysis of shared dysregulated pathways called out by both the mouse and human classifiers of TransPath-C can identify pathways that can be targeted in both preclinical and human cohorts of schizophrenia. In application to the 22q11 MD mouse model, our findings suggest that PAR1 pathway activation found upregulated in this mouse phenotype is germane for the corresponding human schizophrenia cohort such that inhibition of PAR1 may offer a novel therapeutic target.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

Perfluorooctanesulfonic Acid-Induced Toxicity on Zebrafish Embryos in the Presence or Absence of the Chorion

Perfluorooctanesulfonic acid (PFOS) is a perfluorinated compound used in many industrial and consumer products. It has been linked to a broad range of adverse effects in several species, including zebrafish (Danio rerio). The zebrafish embryo is a widely used vertebrate model to elucidate potential adverse effects of chemicals because it is amenable to medium and high throughput. However, there is limited research on the full extent of the impact the chorion has on those effects. Results from the present study indicate that the presence of the chorion affected the timing and incidence of mortality as well as morphometric endpoints such as spinal curvature and swim bladder inflation in zebrafish embryos exposed to PFOS. Furthermore, removal of the chorion prior to exposure resulted in a lower threshold of sensitivity to PFOS for effects on transcriptional expression within the peroxisome proliferator-activated receptor (PPAR) nuclear signaling pathway. Perturbation of PPAR pathway gene expression can result in disruption of metabolic signaling and regulation, which can adversely affect development, energy availability, and survival. It can be concluded that removal of the chorion has significant effects on the timing and incidence of impacts associated with PFOS exposure, and more research is warranted to fully elucidate the protective role of the chorion and the critical timing of these events. Environ Toxicol Chem 2021;40:780-791. Published 2020. This article is a US Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Stochastically modeling multiscale stationary biological processes

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

AOPERA: A proposed methodology and inventory of effective tools to link chemicals to adverse outcome pathways

New approaches, like the Adverse Outcome Pathway (AOP) framework, have been developed to describe how chemicals cause toxicity by linking in vitro assays to adverse health outcomes. However, approaches, tools and resources for development of AOPs have not been well described. Here we review information resources for AOP development and define a streamlined process for linking a chemical to an existing AOP. We propose a four step process to facilitate AOP development: link the uncharacterized chemical directly to Molecular Initiating Events, Key Events, or Adverse Outcomes; identify analogs with toxicological information for the uncharacterized chemical; link the characterized chemical (initial chemical if characterized, a characterized analog if initial chemical is not) to Molecular Initiating Events, Key Events, or Adverse Outcomes; and identify AOPs that contain the Molecular Initiating Events, Key Events, or Adverse Outcomes that were found in Steps 1 and 3. The process and library of informational resources proposed and tested here served as the foundation for an informational online tool (AOPERA) that helps practitioners identify their current-state knowledge gaps, navigate the four-step process, and connect to relevant resources. AOPERA can be found at https://igbb.github.io/AOPERA_HTML. Additionally, we anticipate that by simplifying and standardizing the process of linking a chemical to a known AOP, we will lower the barrier to entry for this objective and increase its accessibility to new practitioners. In turn, this may increase the demand for new or improved AOPs to which practitioners can link chemicals, thereby contributing to the expansion of the library of known AOPs.

Difficult airway management algorithms: a directed review

The primary aim of this study was to identify, describe and compare the content of existing difficult airway management algorithms. Secondly, we aimed to describe the literature reporting the implementation of these algorithms. A directed search across three databases (MEDLINE, Embase and Scopus) was performed. All articles were screened for relevance to the research aims and according to pre-determined exclusion criteria. We identified 38 published airway management algorithms. Our results show that most facemask employ a four-step process as represented by a flow chart, with progression from tracheal intubation, facemask ventilation and supraglottic airway device use, to a rescue emergency surgical airway. The identified algorithms are overwhelmingly similar, yet many use differing terminology. The frequency of algorithm publication has increased recently, yet adherence and implementation outcome data remain limited. Our results highlight the lack of a single algorithm that is universally endorsed, recognised and applicable to all difficult airway management situations.

Keanu: a novel visualization tool to explore biodiversity in metagenomes

Background

One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier.

Results

Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska.

Conclusions

Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments. Keanu is written in Python and is freely available at https://github.com/IGBB/keanu.

Multiple environmental stressors induce complex transcriptomic responses indicative of phenotypic outcomes in Western fence lizard

BACKGROUND

The health and resilience of species in natural environments is increasingly challenged by complex anthropogenic stressor combinations including climate change, habitat encroachment, and chemical contamination. To better understand impacts of these stressors we examined the individual- and combined-stressor impacts of malaria infection, food limitation, and 2,4,6-trinitrotoluene (TNT) exposures on gene expression in livers of Western fence lizards (WFL, Sceloporus occidentalis) using custom WFL transcriptome-based microarrays.

RESULTS

Computational analysis including annotation enrichment and correlation analysis identified putative functional mechanisms linking transcript expression and toxicological phenotypes. TNT exposure increased transcript expression for genes involved in erythropoiesis, potentially in response to TNT-induced anemia and/or methemoglobinemia and caused dose-specific effects on genes involved in lipid and overall energy metabolism consistent with a hormesis response of growth stimulation at low doses and adverse decreases in lizard growth at high doses. Functional enrichment results were indicative of inhibited potential for lipid mobilization and catabolism in TNT exposures which corresponded with increased inguinal fat weights and was suggestive of a decreased overall energy budget. Malaria infection elicited enriched expression of multiple immune-related functions likely corresponding to increased white blood cell (WBC) counts. Food limitation alone enriched functions related to cellular energy production and decreased expression of immune responses consistent with a decrease in WBC levels.

CONCLUSIONS

Despite these findings, the lizards demonstrated immune resilience to malaria infection under food limitation with transcriptional results indicating a fully competent immune response to malaria, even under bio-energetic constraints. Interestingly, both TNT and malaria individually increased transcriptional expression of immune-related genes and increased overall WBC concentrations in blood; responses that were retained in the TNT x malaria combined exposure. The results demonstrate complex and sometimes unexpected responses to multiple stressors where the lizards displayed remarkable resiliency to the stressor combinations investigated.

Constitutive behaviour of paddlefish (Polyodon spathula) cartilage

This study explored the constitutive behaviour of the midline cartilage of the paddlefish (Polyodon spathula) rostrum and examined the properties of the cartilage as a function of stress state and strain rate. The cartilage structure of the paddlefish contributes to the physical characteristics of the unique rostrum, and is of much interest as an inspiration for engineered structures. Low-strain rate testing (∼0·01/s) results indicated an average compressive yield stress of 11·34 ± 3·10 MPa and an average tensile yield stress of 3·62 ± 0·62 MPa. High-strain rate testing (∼100/s) results showed an average compressive yield stress of 25·07 ± 10·86 MPa. A histological study showed that the material is a type of hyaline cartilage. An Ogden hyperelastic model was used for finite-element analysis and fit very well with experimental data. This paper contributes to an overall study examining the electrosensory capabilities, hydrodynamics and structure–property relationships of the rostrum.

Rapid effects of the aromatase inhibitor fadrozole on steroid production and gene expression in the ovary of female fathead minnows (Pimephales promelas)

Cytochrome P450 aromatase catalyzes conversion of C19 androgens to C18 estrogens and is critical for normal reproduction in female vertebrates. Fadrozole is a model aromatase inhibitor that has been shown to suppress estrogen production in the ovaries of fish. However, little is known about the early impacts of aromatase inhibition on steroid production and gene expression in fish. Adult female fathead minnows (Pimephales promelas) were exposed via water to 0, 5, or 50µg fadrozole/L for a time-course of 0.5, 1, 2, 4, and 6h, or 0 or 50µg fadrozole/L for a time-course of 6, 12, and 24h. We examined ex vivo ovarian 17β-estradiol (E2) and testosterone (T) production, and plasma E2 concentrations from each study. Expression profiles of genes known or hypothesized to be impacted by fadrozole including aromatase (cytochrome P450 [cyp] 19a1a), steriodogenic acute regulatory protein (star), cytochrome P450 side-chain cleavage (cyp11a), cytochrome P450 17 alpha hydroxylase/17,20 lyase (cyp17), and follicle stimulating hormone receptor (fshr) were measured in the ovaries by quantitative real-time polymerase chain reaction (QPCR). In addition, broader ovarian gene expression was examined using a 15k fathead minnow microarray. The 5µg/L exposure significantly reduced ex vivo E2 production by 6h. In the 50µg/L treatment, ex vivo E2 production was significantly reduced after just 2h of exposure and remained depressed at all time-points examined through 24h. Plasma E2 concentrations were significantly reduced as early as 4h after initiation of exposure to either 5 or 50µg fadrozole/L and remained depressed throughout 24h in the 50µg/L exposure. Ex vivo T concentrations remained unchanged throughout the time-course. Expression of transcripts involved in steroidogenesis increased within the first 24h suggesting rapid induction of a mechanism to compensate for fadrozole inhibition of aromatase. Microarray results also showed fadrozole exposure caused concentration- and time-dependent changes in gene expression profiles in many HPG-axis pathways as early as 4h. This study provides insights into the very rapid effects of aromatase inhibition on steroidogenic processes in fish.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

Prioritization of Contaminants of Emerging Concern in Wastewater Treatment Plant Discharges Using Chemical:Gene Interactions in Caged Fish

We examined whether contaminants present in surface waters could be prioritized for further assessment by linking the presence of specific chemicals to gene expression changes in exposed fish. Fathead minnows were deployed in cages for 2, 4, or 8 days at three locations near two different wastewater treatment plant discharge sites in the Saint Louis Bay, Duluth, MN and one upstream reference site. The biological impact of 51 chemicals detected in the surface water of 133 targeted chemicals was determined using biochemical endpoints, exposure activity ratios for biological and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge bases, and analysis of the covariance of ovary gene expression with surface water chemistry. Thirty-two chemicals were significantly linked by covariance with expressed genes. No estrogenic impact on biochemical endpoints was observed in male or female minnows. However, bisphenol A (BPA) was identified by chemical:gene covariation as the most impactful estrogenic chemical across all exposure sites. This was consistent with identification of estrogenic effects on gene expression, high BPA exposure activity ratios across all test sites, and historical analysis of the study area. Gene expression analysis also indicated the presence of nontargeted chemicals including chemotherapeutics consistent with a local hospital waste stream. Overall impacts on gene expression appeared to be related to changes in treatment plant function during rain events. This approach appears useful in examining the impacts of complex mixtures on fish and offers a potential route in linking chemical exposure to adverse outcomes that may reduce population sustainability.

Advancing the adverse outcome pathway framework-An international horizon scanning approach

Our ability to conduct whole-organism toxicity tests to understand chemical safety has been outpaced by the synthesis of new chemicals for a wide variety of commercial applications. As a result, scientists and risk assessors are turning to mechanistically based studies to increase efficiencies in chemical risk assessment and making greater use of in vitro and in silico methods to evaluate potential environmental and human health hazards. In this context, the adverse outcome pathway (AOP) framework has gained traction in regulatory science because it offers an efficient and effective means for capturing available knowledge describing the linkage between mechanistic data and the apical toxicity end points required for regulatory assessments. A number of international activities have focused on AOP development and various applications to regulatory decision-making. These initiatives have prompted dialogue between research scientists and regulatory communities to consider how best to use the AOP framework. Although expert-facilitated discussions and AOP development have been critical in moving the science of AOPs forward, it was recognized that a survey of the broader scientific and regulatory communities would aid in identifying current limitations while guiding future initiatives for the AOP framework. To that end, a global horizon scanning exercise was conducted to solicit questions concerning the challenges or limitations that must be addressed to realize the full potential of the AOP framework in research and regulatory decision-making. The questions received fell into several broad topical areas: AOP networks, quantitative AOPs, collaboration on and communication of AOP knowledge, AOP discovery and development, chemical and cross-species extrapolation, exposure/toxicokinetics considerations, and AOP applications. Expert ranking was then used to prioritize questions for each category, where 4 broad themes emerged that could help inform and guide future AOP research and regulatory initiatives. In addition, frequently asked questions were identified and addressed by experts in the field. Answers to frequently asked questions will aid in addressing common misperceptions and will allow for clarification of AOP topics. The need for this type of clarification was highlighted with surprising frequency by our question submitters, indicating that improvements are needed in communicating the AOP framework among the scientific and regulatory communities. Overall, horizon scanning engaged the global scientific community to help identify key questions surrounding the AOP framework and guide the direction of future initiatives. Environ Toxicol Chem 2017;36:1411-1421. © 2017 SETAC.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Background

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system.

Results

We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements.

Conclusions

Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0407-3) contains supplementary material, which is available to authorized users.

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.

Effects of the antimicrobial contaminant triclocarban, and co-exposure with the androgen 17β-trenbolone, on reproductive function and ovarian transcriptome of the fathead minnow (Pimephales promelas)

Triclocarban (TCC) is an antimicrobial agent routinely detected in surface waters that has been hypothesized to interact with the vertebrate endocrine system. The present study examined the effects of TCC alone and in combination with the model endocrine disruptor 17β-trenbolone (TRB) on fish reproductive function. Adult Pimephales promelas were continuously exposed to either 1 µg TCC/L or 5 µg TCC/L, to 0.5 µg TRB/L, or to a mixture (MIX) of 5 µg TCC/L and 0.5 µg TRB/L for 22 d, and a variety of reproductive and endocrine-related endpoints were examined. Cumulative fecundity was significantly reduced in fathead minnows exposed to TRB, MIX, or 5 µg TCC/L. Exposure to 1 µg TCC/L had no effect on reproduction. In general, both TRB and MIX treatments caused similar physiological effects, evoking significant reductions in female plasma vitellogenin, estradiol, and testosterone, and significant increases in male plasma estradiol. Based on analysis of the ovarian transcriptome, there were potential pathway impacts that were common to both TRB- and TCC-containing treatment groups. In most cases, however, those pathways were more plausibly linked to differences in reproductive status than to androgen-specific functions. Overall, TCC was reproductively toxic to fish at concentrations at or near those that have been measured in surface water. There was little evidence that TCC elicits reproductive toxicity through a specific mode of endocrine or reproductive action, nor that it could augment the androgenic effects of TRB. Nonetheless, the relatively small margin of safety between some measured environmental concentrations and effect concentrations suggests that concern is warranted. Environ Toxicol Chem 2017;36:231-242. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

BACKGROUND

The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions.

OBJECTIVE

Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures.

METHODS

New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.

DISCUSSION

NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams.

CONCLUSIONS

While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence

Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.

Daphnia magna's sense of competition: intra-specific interactions (ISI) alter life history strategies and increase metals toxicity

This work investigates whether the scale-up to multi-animal exposures that is commonly applied in genomics studies provides equivalent toxicity outcomes to single-animal experiments of standard Daphnia magna toxicity assays. Specifically, we tested the null hypothesis that intraspecific interactions (ISI) among D. magna have neither effect on the life history strategies of this species, nor impact toxicological outcomes in exposure experiments with Cu and Pb. The results show that ISI significantly increased mortality of D. magna in both Cu and Pb exposure experiments, decreasing 14 day LC50 s and 95 % confidence intervals from 14.5 (10.9-148.3) to 8.4 (8.2-8.7) µg Cu/L and from 232 (156-4810) to 68 (63-73) µg Pb/L. Additionally, ISI potentiated Pb impacts on reproduction eliciting a nearly 10-fold decrease in the no-observed effect concentration (from 236 to 25 µg/L). As an indication of environmental relevance, the effects of ISI on both mortality and reproduction in Pb exposures were sustained at both high and low food rations. Furthermore, even with a single pair of Daphnia, ISI significantly increased (p < 0.05) neonate production in control conditions, demonstrating that ISI can affect life history strategy. Given these results we reject the null hypothesis and conclude that results from scale-up assays cannot be directly applied to observations from single-animal assessments in D. magna. We postulate that D. magna senses chemical signatures of conspecifics which elicits changes in life history strategies that ultimately increase susceptibility to metal toxicity.

Predicting chemical bioavailability using microarray gene expression data and regression modeling: A tale of three explosive compounds

Background

Chemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation.

Results

This refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R² = 0.71–0.82 was achievable at a relatively low model complexity with as few as 3–10 predictor genes per model. These results are much more encouraging than our previous ones.

Conclusion

This study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2541-5) contains supplementary material, which is available to authorized users.

Ecotoxicogenomics: Microarray interlaboratory comparability

Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, and by identifying sub-lethal organismal responses and contaminant classes underlying observed toxicity. Before transcriptomic information can be used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps impacting the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories with estuarine amphipods exposed to cyfluthrin-spiked or control sediments (10 days). Two sample types were generated, one consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the other consisted of exposed and control whole body amphipods (WB) from which each laboratory extracted RNA. Our findings indicate that gene expression microarray results are repeatable. Differentially expressed data had a higher degree of repeatability across all laboratories in samples with similar RNA quality (Ex) when compared to WB samples with more variable RNA quality. Despite such variability a subset of genes were consistently identified as differentially expressed across all laboratories and sample types. We found that the differences among the individual laboratory results can be attributed to several factors including RNA quality and technical expertise, but the overall results can be improved by following consistent protocols and with appropriate training.

Fish connectivity mapping: linking chemical stressors by their mechanisms of action-driven transcriptomic profiles

Background

A very large and rapidly growing collection of transcriptomic profiles in public repositories is potentially of great value to developing data-driven bioinformatics applications for toxicology/ecotoxicology. Modeled on human connectivity mapping (Cmap) in biomedical research, this study was undertaken to investigate the utility of an analogous Cmap approach in ecotoxicology. Over 3500 zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) transcriptomic profiles, each associated with one of several dozen chemical treatment conditions, were compiled into three distinct collections of rank-ordered gene lists (ROGLs) by species and microarray platforms. Individual query signatures, each consisting of multiple gene probes differentially expressed in a chemical condition, were used to interrogate the reference ROGLs.

Results

Informative connections were established at high success rates within species when, as defined by their mechanisms of action (MOAs), both query signatures and ROGLs were associated with the same or similar chemicals. Thus, a simple query signature functioned effectively as an exposure biomarker without need for a time-consuming process of development and validation. More importantly, a large reference database of ROGLs also enabled a query signature to cross-interrogate other chemical conditions with overlapping MOAs, leading to novel groupings and subgroupings of seemingly unrelated chemicals at a finer resolution. This approach confirmed the identities of several estrogenic chemicals, as well as a polycyclic aromatic hydrocarbon and a neuro-toxin, in the largely uncharacterized water samples near several waste water treatment plants, and thus demonstrates its future potential utility in real world applications.

Conclusions

The power of Cmap should grow as chemical coverages of ROGLs increase, making it a framework easily scalable in the future. The feasibility of toxicity extrapolation across fish species using Cmap needs more study, however, as more gene expression profiles linked to chemical conditions common to multiple fish species are needed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2406-y) contains supplementary material, which is available to authorized users.

Predicting Fecundity of Fathead Minnows (Pimephales promelas) Exposed to Endocrine-Disrupting Chemicals Using a MATLAB®-Based Model of Oocyte Growth Dynamics

Fish spawning is often used as an integrated measure of reproductive toxicity, and an indicator of aquatic ecosystem health in the context of forecasting potential population-level effects considered important for ecological risk assessment. Consequently, there is a need for flexible, widely-applicable, biologically-based models that can predict changes in fecundity in response to chemical exposures, based on readily measured biochemical endpoints, such as plasma vitellogenin (VTG) concentrations, as input parameters. Herein we describe a MATLAB® version of an oocyte growth dynamics model for fathead minnows (Pimephales promelas) with a graphical user interface based upon a previously published model developed with MCSim software and evaluated with data from fathead minnows exposed to an androgenic chemical, 17β-trenbolone. We extended the evaluation of our new model to include six chemicals that inhibit enzymes involved in steroid biosynthesis: fadrozole, ketoconazole, propiconazole, prochloraz, fenarimol, and trilostane. In addition, for unexposed fathead minnows from group spawning design studies, and those exposed to the six chemicals, we evaluated whether the model is capable of predicting the average number of eggs per spawn and the average number of spawns per female, which was not evaluated previously. The new model is significantly improved in terms of ease of use, platform independence, and utility for providing output in a format that can be used as input into a population dynamics model. Model-predicted minimum and maximum cumulative fecundity over time encompassed the observed data for fadrozole and most propiconazole, prochloraz, fenarimol and trilostane treatments, but did not consistently replicate results from ketoconazole treatments. For average fecundity (eggs•female(-1)•day(-1)), eggs per spawn, and the number of spawns per female, the range of model-predicted values generally encompassed the experimentally observed values. Overall, we found that the model predicts reproduction metrics robustly and its predictions capture the variability in the experimentally observed data.

Ionotropic GABA receptor antagonism-induced adverse outcome pathways for potential neurotoxicity biomarkers

Antagonism of ionotropic GABA receptors (iGABARs) can occur at three distinct types of receptor binding sites causing chemically induced epileptic seizures. Here we review three adverse outcome pathways, each characterized by a specific molecular initiating event where an antagonist competitively binds to active sites, negatively modulates allosteric sites or noncompetitively blocks ion channel on the iGABAR. This leads to decreased chloride conductance, followed by depolarization of affected neurons, epilepsy-related death and ultimately decreased population. Supporting evidence for causal linkages from the molecular to population levels is presented and differential sensitivity to iGABAR antagonists in different GABA receptors and organisms discussed. Adverse outcome pathways are poised to become important tools for linking mechanism-based biomarkers to regulated outcomes in next-generation risk assessment.

Transcriptional Network Growing Models Using Motif-Based Preferential Attachment

Understanding relationships between architectural properties of gene-regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics, as it can provide insights into, e.g., disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs – i.e., small-node subgraphs that occur more abundantly in GRNs than expected from chance alone. Because these transcriptional modules represent “building blocks” of complex networks and exhibit a wide range of functional and dynamical properties, they may contribute to the remarkable robustness and dynamical stability associated with the whole of GRNs. Here, we developed network-construction models to better understand this relationship, which produce randomized GRNs by using transcriptional motifs as the fundamental growth unit in contrast to other methods that construct similar networks on a node-by-node basis. Because this model produces networks with a prescribed lower bound on the number of choice transcriptional motifs (e.g., downlinks, feed-forward loops), its fidelity to the motif distributions observed in model organisms represents an improvement over existing methods, which we validated by contrasting their resultant motif and degree distributions against existing network-growth models and data from the model organism of the bacterium Escherichia coli. These models may therefore serve as novel testbeds for further elucidating relationships between the topology of transcriptional motifs and network-wide dynamical properties.

Relating suborganismal processes to ecotoxicological and population level endpoints using a bioenergetic model

Ecological effects of environmental stressors are commonly evaluated using organismal or suborganismal data, such as standardized toxicity tests that characterize responses of individuals (e.g., mortality and reproduction) and a rapidly growing body of "omics" data. A key challenge for environmental risk assessment is relating such information to population dynamics. One approach uses dynamic energy budget (DEB) models that relate growth and reproduction of individuals to underlying flows of energy and elemental matter. We hypothesize that suborganismal information identifies DEB parameters that are most likely impacted by a particular stressor and that the DEB model can then project suborganismal effects on life history and population endpoints. We formulate and parameterize a model of growth and reproduction for the water flea Daphnia magna. Our model resembles previous generic bioenergetic models, but has explicit representation of discrete molts, an important feature of Daphnia life history. We test its ability to predict six endpoints commonly used in chronic toxicity studies in specified food environments. With just one adjustable parameter, the model successfully predicts growth and reproduction of individuals from a wide array of experiments performed in multiple laboratories using different clones of D. magna raised on different food sources. Fecundity is the most sensitive endpoint, and there is broad correlation between the sensitivities of fecundity and long-run growth rate, as is desirable for the default metric used in chronic toxicity tests. Under some assumptions, we can combine our DEB model with the Euler-Lotka equation to estimate longrun population growth rates at different food levels. A review of Daphnia gene-expression experiments on the effects of contaminant exposure reveals several connections to model parameters, in particular a general trend of increased transcript expression of genes involved in energy assimilation and utilization at concentrations affecting growth and reproduction. The sensitivity of fecundity to many model parameters was consistent with frequent generalized observations of decreased expression of genes involved in reproductive physiology, but interpretation of these observations requires further mechanistic modeling. We thus propose an approach based on generic DEB models incorporating few essential species-specific features for rapid extrapolation of ecotoxicogenomic assays for Daphnia-based population risk assessment.

Systems toxicology identifies mechanistic impacts of 2-amino-4,6-dinitrotoluene (2A-DNT) exposure in Northern Bobwhite

Background

A systems toxicology investigation comparing and integrating transcriptomic and proteomic results was conducted to develop holistic effects characterizations for the wildlife bird model, Northern bobwhite (Colinus virginianus) dosed with the explosives degradation product 2-amino-4,6-dinitrotoluene (2A-DNT). A subchronic 60d toxicology bioassay was leveraged where both sexes were dosed via daily gavage with 0, 3, 14, or 30 mg/kg-d 2A-DNT. Effects on global transcript expression were investigated in liver and kidney tissue using custom microarrays for C. virginianus in both sexes at all doses, while effects on proteome expression were investigated in liver for both sexes and kidney in males, at 30 mg/kg-d.

Results

As expected, transcript expression was not directly indicative of protein expression in response to 2A-DNT. However, a high degree of correspondence was observed among gene and protein expression when investigating higher-order functional responses including statistically enriched gene networks and canonical pathways, especially when connected to toxicological outcomes of 2A-DNT exposure. Analysis of networks statistically enriched for both transcripts and proteins demonstrated common responses including inhibition of programmed cell death and arrest of cell cycle in liver tissues at 2A-DNT doses that caused liver necrosis and death in females. Additionally, both transcript and protein expression in liver tissue was indicative of induced phase I and II xenobiotic metabolism potentially as a mechanism to detoxify and excrete 2A-DNT. Nuclear signaling assays, transcript expression and protein expression each implicated peroxisome proliferator-activated receptor (PPAR) nuclear signaling as a primary molecular target in the 2A-DNT exposure with significant downstream enrichment of PPAR-regulated pathways including lipid metabolic pathways and gluconeogenesis suggesting impaired bioenergetic potential.

Conclusion

Although the differential expression of transcripts and proteins was largely unique, the consensus of functional pathways and gene networks enriched among transcriptomic and proteomic datasets provided the identification of many critical metabolic functions underlying 2A-DNT toxicity as well as impaired PPAR signaling, a key molecular initiating event known to be affected in di- and trinitrotoluene exposures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1798-4) contains supplementary material, which is available to authorized users.

Systematic Omics Analysis Review (SOAR) tool to support risk assessment

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.

MicroRNA and messenger RNA profiling reveals new biomarkers and mechanisms for RDX induced neurotoxicity

Background

RDX is a well-known pollutant to induce neurotoxicity. MicroRNAs (miRNA) and messenger RNA (mRNA) profiles are useful tools for toxicogenomics studies. It is worthy to integrate MiRNA and mRNA expression data to understand RDX-induced neurotoxicity.

Results

Rats were treated with or without RDX for 48 h. Both miRNA and mRNA profiles were conducted using brain tissues. Nine miRNAs were significantly regulated by RDX. Of these, 6 and 3 miRNAs were up- and down-regulated respectively. The putative target genes of RDX-regulated miRNAs were highly nervous system function genes and pathways enriched. Fifteen differentially genes altered by RDX from mRNA profiles were the putative targets of regulated miRNAs. The induction of miR-71, miR-27ab, miR-98, and miR-135a expression by RDX, could reduce the expression of the genes POLE4, C5ORF13, SULF1 and ROCK2, and eventually induce neurotoxicity. Over-expression of miR-27ab, or reduction of the expression of unknown miRNAs by RDX, could up-regulate HMGCR expression and contribute to neurotoxicity. RDX regulated immune and inflammation response miRNAs and genes could contribute to RDX- induced neurotoxicity and other toxicities as well as animal defending reaction response to RDX exposure.

Conclusions

Our results demonstrate that integrating miRNA and mRNA profiles is valuable to indentify novel biomarkers and molecular mechanisms for RDX-induced neurological disorder and neurotoxicity.

Natural Variation in Fish Transcriptomes: Comparative Analysis of the Fathead Minnow (Pimephales promelas) and Zebrafish (Danio rerio)

Fathead minnow and zebrafish are among the most intensively studied fish species in environmental toxicogenomics. To aid the assessment and interpretation of subtle transcriptomic effects from treatment conditions of interest, better characterization and understanding are needed for natural variation in gene expression among fish individuals from lab cultures. Leveraging the transcriptomics data from a number of our toxicogenomics studies conducted over the years, we conducted a meta-analysis of nearly 600 microarrays generated from the ovary tissue of untreated, reproductively mature fathead minnow and zebrafish samples. As expected, there was considerable batch-to-batch transcriptomic variation; this “batch-effect” appeared to differentially impact subsets of fish transcriptomes in a nonsystematic way. Temporally more closely spaced batches tended to share a greater transcriptomic similarity among one another. The overall level of within-batch variation was quite low in fish ovary tissue, making it a suitable system for studying chemical stressors with subtle biological effects. The observed differences in the within-batch variability of gene expression, at the levels of both individual genes and pathways, were probably both technical and biological. This suggests that biological interpretation and prioritization of genes and pathways targeted by experimental conditions should take into account both their intrinsic variability and the size of induced transcriptional changes. There was significant conservation of both the genomes and transcriptomes between fathead minnow and zebrafish. The high degree of conservation offers promising opportunities in not only studying fish molecular responses to environmental stressors by a comparative biology approach, but also effective sharing of a large amount of existing public transcriptomics data for developing toxicogenomics applications.

Top-level dynamics and the regulated gene response of feed-forward loop transcriptional motifs

Feed-forward loops are hierarchical three-node transcriptional subnetworks, wherein a top-level protein regulates the activity of a target gene via two paths: a direct-regulatory path, and an indirect route, whereby the top-level proteins act implicitly through an intermediate transcription factor. Using a transcriptional network of the model bacterium Escherichia coli, we confirmed that nearly all types of feed-forward loop were significantly overrepresented in the bacterial network. We then used mathematical modeling to study their dynamics by manipulating the rise times of the top-level protein concentration, termed the induction time, through alteration of the protein destruction rates. Rise times of the regulated proteins exhibited two qualitatively different regimes, depending on whether top-level inductions were "fast" or "slow." In the fast regime, rise times were nearly independent of rapid top-level inductions, indicative of biological robustness, and occurred when RNA production rate-limits the protein yield. Alternatively, the protein rise times were dependent upon slower top-level inductions, greater than approximately one bacterial cell cycle. An equation is given for this crossover, which depends upon three parameters of the direct-regulatory path: transcriptional cooperation at the DNA-binding site, a protein-DNA dissociation constant, and the relative magnitude of the top-level protien concentration.

Gene regulatory network inference and validation using relative change ratio analysis and time-delayed dynamic Bayesian network

The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our approach were evaluated comparatively along with 29 other submissions by two metrics (area under the ROC curve and area under the precision-recall curve). The overall performance of our approach ranked the second among all participating teams.

Coral-zooxanthellae meta-transcriptomics reveals integrated response to pollutant stress

BACKGROUND

Corals represent symbiotic meta-organisms that require harmonization among the coral animal, photosynthetic zooxanthellae and associated microbes to survive environmental stresses. We investigated integrated-responses among coral and zooxanthellae in the scleractinian coral Acropora formosa in response to an emerging marine pollutant, the munitions constituent, 1,3,5-trinitro-1,3,5 triazine (RDX; 5 day exposures to 0 (control), 0.5, 0.9, 1.8, 3.7, and 7.2 mg/L, measured in seawater).

RESULTS

RDX accumulated readily in coral soft tissues with bioconcentration factors ranging from 1.1 to 1.5. Next-generation sequencing of a normalized meta-transcriptomic library developed for the eukaryotic components of the A. formosa coral holobiont was leveraged to conduct microarray-based global transcript expression analysis of integrated coral/zooxanthellae responses to the RDX exposure. Total differentially expressed transcripts (DET) increased with increasing RDX exposure concentrations as did the proportion of zooxanthellae DET relative to the coral animal. Transcriptional responses in the coral demonstrated higher sensitivity to RDX compared to zooxanthellae where increased expression of gene transcripts coding xenobiotic detoxification mechanisms (i.e. cytochrome P450 and UDP glucuronosyltransferase 2 family) were initiated at the lowest exposure concentration. Increased expression of these detoxification mechanisms was sustained at higher RDX concentrations as well as production of a physical barrier to exposure through a 40% increase in mucocyte density at the maximum RDX exposure. At and above the 1.8 mg/L exposure concentration, DET coding for genes involved in central energy metabolism, including photosynthesis, glycolysis and electron-transport functions, were decreased in zooxanthellae although preliminary data indicated that zooxanthellae densities were not affected. In contrast, significantly increased transcript expression for genes involved in cellular energy production including glycolysis and electron-transport pathways was observed in the coral animal.

CONCLUSIONS

Transcriptional network analysis for central energy metabolism demonstrated highly correlated responses to RDX among the coral animal and zooxanthellae indicative of potential compensatory responses to lost photosynthetic potential within the holobiont. These observations underscore the potential for complex integrated responses to RDX exposure among species comprising the coral holobiont and highlight the need to understand holobiont-species interactions to accurately assess pollutant impacts.

Integrated approach to explore the mechanisms of aromatase inhibition and recovery in fathead minnows (Pimephales promelas)

Aromatase, a member of the cytochrome P450 superfamily, is a key enzyme in estradiol synthesis that catalyzes the aromatization of androgens into estrogens in ovaries. Here, we used an integrated approach to assess the mechanistic basis of the direct effects of aromatase inhibition, as well as adaptation and recovery processes in fish. We exposed female fathead minnows (Pimephales promelas) via the water to 30 μg/L of a model aromatase inhibitor, fadrozole, during 8 days (exposure phase). Fish were then held in clean water for 8 more days (recovery phase). Samples were collected at 1, 2, 4, and 8 days of both the exposure and the recovery phases. Transcriptomics, metabolomics, and network inference were used to understand changes and infer connections at the transcript and metabolite level in the ovary. Apical endpoints directly indicative of endocrine function, such as plasma estradiol, testosterone, and vitellogenin levels were also measured. An integrated analysis of the data revealed changes in gene expression consistent with increased testosterone in fadrozole-exposed ovaries. Metabolites such as glycogen and taurine were strongly correlated with increased testosterone levels. Comparison of in vivo and ex vivo steroidogenesis data suggested the accumulation of steroidogenic enzymes, including aromatase, as a mechanism to compensate for aromatase inhibition.

Endocrinology: advances through omics and related technologies

The rapid development of new omics technologies to measure changes at genetic, transcriptomic, proteomic, and metabolomics levels together with the evolution of methods to analyze and integrate the data at a systems level are revolutionizing the study of biological processes. Here we discuss how new approaches using omics technologies have expanded our knowledge especially in nontraditional models. Our increasing knowledge of these interactions and evolutionary pathway conservation facilitates the use of nontraditional species, both invertebrate and vertebrate, as new model species for biological and endocrinology research. The increasing availability of technology to create organisms overexpressing key genes in endocrine function allows manipulation of complex regulatory networks such as growth hormone (GH) in transgenic fish where disregulation of GH production to produce larger fish has also permitted exploration of the role that GH plays in testis development, suggesting that it does so through interactions with insulin-like growth factors. The availability of omics tools to monitor changes at nearly any level in any organism, manipulate gene expression and behavior, and integrate data across biological levels, provides novel opportunities to explore endocrine function across many species and understand the complex roles that key genes play in different aspects of the endocrine function.

Validation of a genomics-based hypothetical adverse outcome pathway: 2,4-dinitrotoluene perturbs PPAR signaling thus impairing energy metabolism and exercise endurance

2,4-dinitrotoluene (2,4-DNT) is a nitroaromatic used in industrial dyes and explosives manufacturing processes that is found as a contaminant in the environment. Previous studies have implicated antagonism of PPARα signaling as a principal process affected by 2,4-DNT. Here, we test the hypothesis that 2,4-DNT-induced perturbations in PPARα signaling and resultant downstream deficits in energy metabolism, especially from lipids, cause organism-level impacts on exercise endurance. PPAR nuclear activation bioassays demonstrated inhibition of PPARα signaling by 2,4-DNT whereas PPARγ signaling increased. PPARα (-/-) and wild-type (WT) female mice were exposed for 14 days to vehicle or 2,4-DNT (134 mg/kg/day) and performed a forced swim to exhaustion 1 day after the last dose. 2,4-DNT significantly decreased body weights and swim times in WTs, but effects were significantly mitigated in PPARα (-/-) mice. 2,4-DNT decreased transcript expression for genes downstream in the PPARα signaling pathway, principally genes involved in fatty acid transport. Results indicate that PPARγ signaling increased resulting in enhanced cycling of lipid and carbohydrate substrates into glycolytic/gluconeogenic pathways favoring energy production versus storage in 2,4-DNT-exposed WT and PPARα (-/-) mice. PPARα (-/-) mice appear to have compensated for the loss of PPARα by shifting energy metabolism to PPARα-independent pathways resulting in lower sensitivity to 2,4-DNT when compared with WT mice. Our results validate 2,4-DNT-induced perturbation of PPARα signaling as the molecular initiating event for impaired energy metabolism, weight loss, and decreased exercise performance.

Differential effects and potential adverse outcomes of ionic silver and silver nanoparticles in vivo and in vitro

Nanoparticles are of concern because of widespread use, but it is unclear if metal nanoparticles cause effects directly or indirectly. We explored whether polyvinylpyrrolidone-coated silver nanoparticles (PVP-AgNPs) cause effects through intact nanoparticles or dissolved silver. Females of the model species fathead minnow (Pimephales promelas) were exposed to either 4.8 μg/L of AgNO3 or 61.4 μg/L of PVP-AgNPs for 96h. Microarray analyses were used to identify impacted receptors and toxicity pathways in liver and brain tissues that were confirmed using in vitro mammalian assays. AgNO3 and PVP-AgNP exposed fish had common and distinct effects consistent with both intact nanoparticles and dissolved silver causing effects. PVP-AgNPs and AgNO3 both affected pathways involved in Na(+), K(+), and H(+) homeostasis and oxidative stress but different neurotoxicity pathways. In vivo effects were supported by PVP-AgNP activation of five in vitro nuclear receptor assays and inhibition of ligand binding to the dopamine receptor. AgNO3 inhibited ligand binding to adrenergic receptors α1 and α2 and cannabinoid receptor CB1, but had no effect in nuclear receptor assays. PVP-AgNPs have the potential to cause effects both through intact nanoparticles and metal ions, each interacting with different initiating events. Since the in vitro and in vivo assays examined here are commonly used in human and ecological hazard screening, this work suggests that environmental health assessments should consider effects of intact nanoparticles in addition to dissolved metals.

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

Background

High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results

In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators.

Conclusions

Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.

Using transcriptomic tools to evaluate biological effects across effluent gradients at a diverse set of study sites in Minnesota, USA

The aim of this study was to explore the utility of "omics" approaches in monitoring aquatic environments where complex, often unknown stressors make chemical-specific risk assessment untenable. We examined changes in the fathead minnow (Pimephales promelas) ovarian transcriptome following 4-day exposures conducted at three sites in Minnesota (MN, USA). Within each site, fish were exposed to water from three locations along a spatial gradient relative to a wastewater treatment plant (WWTP) discharge. After exposure, site-specific impacts on gene expression in ovaries were assessed. Using an intragradient point of comparison, biological responses specifically associated with the WWTP effluent were identified using functional enrichment analyses. Fish exposed to water from locations downstream of the effluent discharges exhibited many transcriptomic responses in common with those exposed to the effluent, indicating that effects of the discharge do not fully dissipate downstream. Functional analyses showed a range of biological pathways impacted through effluent exposure at all three sites. Several of those impacted pathways at each site could be linked to potential adverse reproductive outcomes associated with the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows, specifically signaling pathways associated with oocyte meiosis, TGF-beta signaling, gonadotropin-releasing hormone (GnRH) and epidermal growth factor receptor family (ErbB), and gene sets associated with cyclin B-1 and metalloproteinase. The utility of this approach comes from the ability to identify biological responses to pollutant exposure, particularly those that can be tied to adverse outcomes at the population level and those that identify molecular targets for future studies.

Effects of BDE-209 contaminated sediments on zebrafish development and potential implications to human health

Polybrominated diphenyl ethers are compounds widely used as flame-retardants, which are of increasing environmental concern due to their persistence, and potential adverse effects. This study had two objectives. First, we assessed if BDE-209 in sediment was bioavailable and bioaccumulated into zebrafish embryos. Secondly, we assessed the potential impact on human and environmental health of bioavailable BDE-209 using human in vitro cell assays and zebrafish embryos. Zebrafish were exposed from 4h to 8days post-fertilization to sediments spiked with 12.5mg/kg of BDE-209. Zebrafish larvae accumulated ten fold more BDE-209 than controls in unspiked sediment after 8days. BDE-209 impacted expression of neurological pathways and altered behavior of larvae, although BDE-209 had no visible affect on thyroid function or motoneuron and neuromast development. Zebrafish data and in silico predictions suggested that BDE-209 would also interact with key human transcription factors and receptors. We therefore tested these predictions using mammalian in vitro assays. BDE-209 activated human aryl hydrocarbon receptor, peroxisome proliferator activating receptors, CF/b-cat, activator protein 1, Oct-MLP, and the estrogen receptor-related alpha (ERRα) receptor in cell-based assays. BDE-209 also inhibited human acetylcholinesterase activity. The observation that BDE-209 can be bioaccumulated from contaminated sediment highlights the need to consider this as a potential environmental exposure route. Once accumulated, our data also show that BDE-209 has the potential to cause impacts on both human and environmental health.