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.