Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment

Background

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting.

Methods

Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways.

Results

Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach.

Conclusions

Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

Current perspectives on the use of alternative species in human health and ecological hazard assessments

BACKGROUND

Traditional animal toxicity tests can be time and resource intensive, thereby limiting the number of chemicals that can be comprehensively tested for potential hazards to humans and/or to the environment.

OBJECTIVE

We compared several types of data to demonstrate how alternative models can be used to inform both human and ecological risk assessment.

METHODS

We reviewed and compared data derived from high throughput in vitro assays to fish reproductive tests for seven chemicals. We investigated whether human-focused assays can be predictive of chemical hazards in the environment. We examined how conserved pathways enable the use of nonmammalian models, such as fathead minnow, zebrafish, and Xenopus laevis, to understand modes of action and to screen for chemical risks to humans.

RESULTS

We examined how dose-dependent responses of zebrafish embryos exposed to flusilazole can be extrapolated, using pathway point of departure data and reverse toxicokinetics, to obtain human oral dose hazard values that are similar to published mammalian chronic toxicity values for the chemical. We also examined how development/safety data for human health can be used to help assess potential risks of pharmaceuticals to nontarget species in the environment.

DISCUSSION

Using several examples, we demonstrate that pathway-based analysis of chemical effects provides new opportunities to use alternative models (nonmammalian species, in vitro tests) to support decision making while reducing animal use and associated costs.

CONCLUSIONS

These analyses and examples demonstrate how alternative models can be used to reduce cost and animal use while being protective of both human and ecological health.

The good, the bad, and the toxic: approaching hormesis in Daphnia magna exposed to an energetic compound

A hormetic response is characterized by an opposite effect in small and large doses of chemical exposure, often resulting in seemingly beneficial effects at low doses. Here, we examined the potential mechanisms underlying the hormetic response of Daphnia magna to the energetic trinitrotoluene (TNT). Daphnia magna were exposed to TNT for 21 days, and a significant increase in adult length and number of neonates was identified at low concentrations (0.002-0.22 mg/L TNT), while toxic effects were identified at high concentrations (0.97 mg/L TNT and above). Microarray analysis of D. magna exposed to 0.004, 0.12, and 1.85 mg/L TNT identified effects on lipid metabolism as a potential mechanism underlying hormetic effects. Lipidomic analysis of exposed D. magna supported the hypothesis that TNT exposure affected lipid and fatty acid metabolism, showing that hormetic effects could be related to changes in polyunsaturated fatty acids known to be involved in Daphnia growth and reproduction. Our results show that Daphnia exposed to low levels of TNT presented hormetic growth and reproduction enhancement, while higher TNT concentrations had an opposite effect. Our results also show how a systems approach can help elucidate potential mechanisms of action and adverse outcomes.

Interspecific effects of 4A-DNT (4-amino-2,6-dinitrotoluene) and RDX (1,3,5-trinitro-1,3,5-triazine) in Japanese quail, Northern bobwhite, and Zebra finch

The purpose of this study was to assess the toxicological effects of two munition compounds, 4-amino-2,6-dinitrotoluene (4A-DNT) and 1,3,5-trinitro-1,3,5-triazine (RDX), on three different bird species: two common toxicological model species-the Northern Bobwhite (Colinus virginianus) and the Japanese Quail (Coturnix japonica), and a representative passerine-the Zebra Finch (Taeniopygia guttata). Bobwhite were exposed to 4A-DNT at 0, 8, 15, 30, 60, or 150 mg/kg body weight (bw) d by oral gavage for seven days; because the high dose of 4A-DNT was lethal to bobwhite, the maximum dose was changed to 100 mg/kg bw d for Japanese quail and finches to ensure tissue could be used for future toxicogenomic work. RDX was similarly administered at 0, 0.5, 1.5, 3, 6, or 12 mg/kg bw d. Blood was drawn prior to euthanasia for blood cellularity and chemistry analyses. Finches were clearly least affected by 4A-DNT as evidenced by a lack of observable effects. Bobwhite appeared to be the most sensitive species to 4A-DNT as observed through changes in blood cellularity and plasma chemistry effects. Bobwhite appeared to be more sensitive to RDX than Japanese Quail due to increased effects on measures of plasma chemistries. Finches exhibited the greatest sensitivity to RDX through increased mortality and seizure activity. This study suggests that sensitivity among species is chemical-specific and provides data that could be used to refine current avian sensitivity models used in ecological risk assessments.

Multiple environmental stressors elicit complex interactive effects in the western fence lizard (Sceloporus occidentalis)

Evaluation of multiple-stressor effects stemming from habitat degradation, climate change, and exposure to chemical contaminants is crucial for addressing challenges to ecological and environmental health. To assess the effects of multiple stressors in an understudied taxon, the western fence lizard (Sceloporus occidentalis) was used to characterize the individual and combined effects of food limitation, exposure to the munitions constituent 2,4,6-trinitrotoluene (TNT), and Plasmodium mexicanum (lizard malaria) infection. Three experimental assays were conducted including: Experiment I--TNT × Food Limitation, Experiment II--Food Limitation × Malaria Infection, and Experiment III--TNT × Malaria Infection. All experiments had a 30 day duration, the malaria treatment included infected and non infected control lizards, food limitation treatments included an ad libitum control and at least one reduced food ration and TNT exposures consisting of daily oral doses of corn oil control or a corn oil-TNT suspension at 5, 10, 20, 40 mg/kg/day. The individual stressors caused a variety of effects including: reduced feeding, reduced testes mass, anemia, increased white blood cell (WBC) concentrations and increased mass of liver, kidney and spleen in TNT exposures; reduced cholesterol, WBC concentrations and whole body, testes and inguinal fat weights given food limitation; and increased WBC concentrations and spleen weights as well as decreased cholesterol and testes mass in malaria infected lizards. Additive and interactive effects were found among certain stressor combinations including elimination of TNT-induced hormesis for growth under food limitation. Ultimately, our study indicates the potential for effects modulation when environmental stressors are combined.

Motif Participation by Genes in E. coli Transcriptional Networks

Motifs are patterns of recurring connections among the genes of genetic networks that occur more frequently than would be expected from randomized networks with the same degree sequence. Although the abundance of certain three-node motifs, such as the feed-forward loop, is positively correlated with a networks’ ability to tolerate moderate disruptions to gene expression, little is known regarding the connectivity of individual genes participating in multiple motifs. Using the transcriptional network of the bacterium Escherichia coli, we investigate this feature by reconstructing the distribution of genes participating in feed-forward loop motifs from its largest connected network component. We contrast these motif participation distributions with those obtained from model networks built using the preferential attachment mechanism employed by many biological and man-made networks. We report that, although some of these model networks support a motif participation distribution that appears qualitatively similar to that obtained from the bacterium E. coli, the probability for a node to support a feed-forward loop motif may instead be strongly influenced by only a few master transcriptional regulators within the network. From these analyses we conclude that such master regulators may be a crucial ingredient to describe coupling among feed-forward loop motifs in transcriptional regulatory networks.

A systems toxicology approach to elucidate the mechanisms involved in RDX species-specific sensitivity

Interspecies uncertainty factors in ecological risk assessment provide conservative estimates of risk where limited or no toxicity data is available. We quantitatively examined the validity of interspecies uncertainty factors by comparing the responses of zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) to the energetic compound 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), a known neurotoxicant. Relative toxicity was measured through transcriptional, morphological, and behavioral end points in zebrafish and fathead minnow fry exposed for 96 h to RDX concentrations ranging from 0.9 to 27.7 mg/L. Spinal deformities and lethality occurred at 1.8 and 3.5 mg/L RDX respectively for fathead minnow and at 13.8 and 27.7 mg/L for zebrafish, indicating that zebrafish have an 8-fold greater tolerance for RDX than fathead minnow fry. The number and magnitude of differentially expressed transcripts increased with increasing RDX concentration for both species. Differentially expressed genes were enriched in functions related to neurological disease, oxidative-stress, acute-phase response, vitamin/mineral metabolism and skeletal/muscular disorders. Decreased expression of collagen-coding transcripts were associated with spinal deformity and likely involved in sensitivity to RDX. Our work provides a mechanistic explanation for species-specific sensitivity to RDX where zebrafish responded at lower concentrations with greater numbers of functions related to RDX tolerance than fathead minnow. While the 10-fold interspecies uncertainty factor does provide a reasonable cross-species estimate of toxicity in the present study, the observation that the responses between ZF and FHM are markedly different does initiate a call for concern regarding establishment of broad ecotoxicological conclusions based on model species such as zebrafish.

Short-term study investigating the estrogenic potency of diethylstilbesterol in the fathead minnow (Pimephales promelas)

Diethylstilbestrol (DES) is a synthetic estrogen that has been banned for use in humans, but still is employed in livestock and aquaculture operations in some parts of the world. Detectable concentrations of DES in effluent and surface waters have been reported to range from slightly below 1 to greater than 10 ng/L. Little is known, however, concerning the toxicological potency of DES in fish. In this study, sexually mature fathead minnows (Pimephales promelas) of both sexes were exposed to 1, 10, or 100 ng of DES/L of water in a flow-through system. Tissue concentrations of DES and changes in a number of estrogen-responsive end points were measured in the fish at the end of a 4 d exposure and after a 4 d depuration/recovery period in clean water. Accumulation of DES was sex-dependent, with females exhibiting higher tissue residues than males after the 4 d exposure. The observed bioconcentration of DES in the fish was about 1 order of magnitude lower than that predicted on the basis of the octanol-water partition coefficient of the chemical, suggesting relatively efficient metabolic clearance by the fish. Exposure to 1, 10, or 100 ng of DES/L caused decreased testis weight and morphological demasculinization of males (regression of dorsal nuptial tubercles). Diethylstilbesterol induced plasma vitellogenin (VTG) in both sexes at water concentrations ≥10 ng/L; this response (especially in males) persisted through the end of the 4 d recovery period. Hepatic transcripts of VTG and estrogen receptor-α also were affected at DES concentrations ≥10 ng/L. Evaluation of transcript profiles in the liver of females using a 15K-gene fathead minnow microarray revealed a concentration-dependent change in gene expression, with mostly up-regulated transcripts after the exposure and substantial numbers of down-regulated gene products after depuration. Genes previously identified as vitellogenesis-related and regulated by 17β-estradiol were significantly enriched among those differentially expressed following exposure to DES. Overall, our studies show that DES causes a range of responses in fish at water concentrations comparable to those reported in the environment and that in vivo potency of the estrogen is on par with that of the better-studied estrogenic contaminant 17α-ethinylestradiol.

CAPRG: sequence assembling pipeline for next generation sequencing of non-model organisms

Our goal is to introduce and describe the utility of a new pipeline "Contigs Assembly Pipeline using Reference Genome" (CAPRG), which has been developed to assemble "long sequence reads" for non-model organisms by leveraging a reference genome of a closely related phylogenetic relative. To facilitate this effort, we utilized two avian transcriptomic datasets generated using ROCHE/454 technology as test cases for CAPRG assembly. We compared the results of CAPRG assembly using a reference genome with the results of existing methods that utilize de novo strategies such as VELVET, PAVE, and MIRA by employing parameter space comparisons (intra-assembling comparison). CAPRG performed as well or better than the existing assembly methods based on various benchmarks for "gene-hunting." Further, CAPRG completed the assemblies in a fraction of the time required by the existing assembly algorithms. Additional advantages of CAPRG included reduced contig inflation resulting in lower computational resources for annotation, and functional identification for contigs that may be categorized as "unknowns" by de novo methods. In addition to providing evaluation of CAPRG performance, we observed that the different assembly (inter-assembly) results could be integrated to enhance the putative gene coverage for any transcriptomics study.

Gene expression analysis of CL-20-induced reversible neurotoxicity reveals GABA(A) receptors as potential targets in the earthworm Eisenia fetida

The earthworm Eisenia fetida is one of the most used species in standardized soil ecotoxicity tests. End points such as survival, growth, and reproduction are eco-toxicologically relevant but provide little mechanistic insight into toxicity pathways, especially at the molecular level. Here we apply a toxicogenomic approach to investigate the mode of action underlying the reversible neurotoxicity of hexanitrohexaazaisowurtzitane (CL-20), a cyclic nitroamine explosives compound. We developed an E. fetida-specific shotgun microarray targeting 15119 unique E. fetida transcripts. Using this array we profiled gene expression in E. fetida in response to exposure to CL-20. Eighteen earthworms were exposed for 6 days to 0.2 μg/cm(2) of CL-20 on filter paper, half of which were allowed to recover in a clean environment for 7 days. Nine vehicle control earthworms were sacrificed at days 6 and 13, separately. Electrophysiological measurements indicated that the conduction velocity of earthworm medial giant nerve fiber decreased significantly after 6-day exposure to CL-20, but was restored after 7 days of recovery. Total RNA was isolated from the four treatment groups including 6-day control, 6-day exposed, 13-day control, and 13-day exposed (i.e., 6-day exposure followed by 7-day recovery), and was hybridized to the 15K shotgun oligo array. Statistical and bioinformatic analyses suggest that CL-20 initiated neurotoxicity by noncompetitively blocking the ligand-gated GABA(A) receptor ion channel, leading to altered expression of genes involved in GABAergic, cholinergic, and Agrin-MuSK pathways. In the recovery phase, expression of affected genes returned to normality, possibly as a result of autophagy and CL-20 dissociation/metabolism. This study provides significant insights into potential mechanisms of CL-20-induced neurotoxicity and the recovery of earthworms from transient neurotoxicity stress.

Assessment of chemical mixtures and groundwater effects on Daphnia magna transcriptomics

Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, fifteen polymerase chain reaction assays targeting Daphnia magna genes were calibrated to responses elicited in D. magna exposed for 24 h to five different doses each of the munitions constituents 2,4,6-trinitrotoluene, 2,4-dinitrotoluene, 2,6-dinitrotoluene, trinitrobenzene, dinitrobenzene, or 1,3,5-trinitro-1,3,5-triazacyclohexane. A piecewise-linear model for log-fold expression changes in gene assays was used to predict response to munitions mixtures and contaminated groundwater under the assumption that chemical effects were additive. The correlations of model predictions with actual expression changes ranged from 0.12 to 0.78 with an average of 0.5. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. Whereas mixtures and groundwater exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the nonadditivity of chemical effects in these mixtures. These results suggest that, while gene behavior in response to chemical exposure can be partially predicted based on chemical exposure, estimation of the composition of mixtures from chemical responses is difficult without further understanding of gene behavior in mixtures. Future work will need to examine additive and nonadditive mixture effects using a much greater range of different chemical classes in order to clarify the behavior and predictability of complex mixtures.

Ecotoxicogenomics to support ecological risk assessment: a case study with bisphenol A in fish

Effects of bisphenol A (BPA) on ovarian transcript profiles as well as targeted end points with endocrine/reproductive relevance were examined in two fish species, fathead minnow (Pimephales promelas) and zebrafish (Danio rerio), exposed in parallel using matched experimental designs. Four days of waterborne exposure to 10 μg BPA/L caused significant vitellogenin induction in both species. However, zebrafish were less sensitive to effects on hepatic gene expression and steroid production than fathead minnow and the magnitude of vitellogenin induction was more modest (i.e., 3-fold compared to 13,000-fold in fathead minnow). The concentration-response at the ovarian transcriptome level was nonmonotonic and violated assumptions that underlie proposed methods for estimating hazard thresholds from transcriptomic results. However, the nonmonotonic profile was consistent among species and there were nominal similarities in the functions associated with the differentially expressed genes, suggesting potential activation of common pathway perturbation motifs in both species. Overall, the results provide an effective case study for considering the potential application of ecotoxicogenomics to ecological risk assessments and provide novel comparative data regarding effects of BPA in fish.

Building quantitative prediction models for tissue residue of two explosives compounds in earthworms from microarray gene expression data

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic processes including uptake, transformation, and assimilation. The availability of high-dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), in the earthworm Eisenia fetida. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wilcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios.

Learning the structure of gene regulatory networks from time series gene expression data

Background

Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL.

Methods

In this paper, we study a more sophisticated score function for DBN first proposed by Nir Friedman for stationary DBNs structure learning of both initial and transition networks but has not yet been used for reconstruction of GRNs. We implemented Friedman's Bayesian Information Criterion (BIC) score function, modified K2 algorithm to learn Dynamic Bayesian Network structure with the score function and tested the performance of the algorithm for GRN reconstruction with synthetic time series gene expression data generated by GeneNetWeaver and real yeast benchmark experiment data.

Results

We implemented an algorithm for DBN structure learning with Friedman's score function, tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou&Conzen's algorithm. By introducing a stationary correlation between two consecutive time slices, Friedman's score function showed a higher precision and recall than the naive REVEAL algorithm.

Conclusions

Friedman's score metrics for DBN can be used to reconstruct transition networks and has a great potential to improve the accuracy of gene regulatory network structure prediction with time series gene expression datasets.

State Space Model with hidden variables for reconstruction of gene regulatory networks

Background

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.

Method

True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.

Results

Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.

Conclusion

This study provides useful information in handling the hidden variables and improving the inference precision.

First-passage time analysis of a one-dimensional diffusion-reaction model: application to protein transport along DNA

BACKGROUND

Proteins search along the DNA for targets, such as transcription initiation sequences, according to one-dimensional diffusion, which is interrupted by micro- and macro-hopping events and intersegmental transfers that occur under close packing conditions.

RESULTS

A one-dimensional diffusion-reaction model in the form of difference-differential equations is proposed to analyze the nonequilibrium protein sliding kinetics along a segment of bacterial DNA. A renormalization approach is used to derive an expression for the mean first-passage time to arrive at sites downstream of the origin from the occupation probabilities given by the individual transport equations. Monte Carlo simulations are employed to assess the validity of the proposed approach, and all results are interpreted within the context of bacterial transcription.

CONCLUSIONS

Mean first-passage times decrease with increasing reaction rates, indicating that, on average, surviving proteins more rapidly locate downstream targets than their reaction-free counterparts, but at the price of increasing rarity. Two qualitatively different screening regimes are identified according to whether the search process operates under "small" or "large" values for the dissociation rate of the protein-DNA complex. Lower bounds are placed on the overall search time for varying reactive conditions. Good agreement with experimental estimates requires the reaction rate reside near the transition between both screening regimes, suggesting that biology balances a need for rapid searches against maximum exploration during each round of the sliding phase.

RefNetBuilder: a platform for construction of integrated reference gene regulatory networks from expressed sequence tags

Background

Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with non-model organisms, we developed RefNetBuilder, a new platform that allows construction of putative reference pathways or GRNs from expressed sequence tags (ESTs).

Results

RefNetBuilder was designed to have the flexibility to extract and archive pathway or GRN information from public databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). It features sequence alignment tools such as BLAST to allow mapping ESTs to pathways and GRNs in model organisms. A scoring algorithm was incorporated to rank and select the best match for each query EST. We validated RefNetBuilder using DNA sequences of Caenorhabditis elegans, a model organism having manually curated KEGG pathways. Using the earthworm Eisenia fetida as an example, we demonstrated the functionalities and features of RefNetBuilder.

Conclusions

The RefNetBuilder provides a standalone application for building reference GRNs for non-model organisms on a number of operating system platforms with standard desktop computer hardware. As a new bioinformatic tool aimed for constructing putative GRNs for non-model organisms that have only ESTs available, RefNetBuilder is especially useful to explore pathway- or network-related information in these organisms.

Genomic investigation of year-long and multigenerational exposures of fathead minnow to the munitions compound RDX

We assessed the impacts of exposure to an environmentally representative concentration (0.83 mg/L) of the explosive cyclotrimethylenetrinitramine (RDX) on fathead minnows (Pimephales promelas) in one-year and multigenerational bioassays. In the one-year bioassay, impacts were assessed by statistical comparisons of females from breeding groups reared in control or RDX-exposure conditions. The RDX had no significant effect on gonadosomatic index or condition factor assayed at 1 d and at one, three, six, nine, and 12 months. The liver-somatic index was significantly increased versus controls only at the 12-month timepoint. RDX had no significant effect on live-prey capture rates, egg production, or fertilization. RDX caused minimal differential-transcript expression with no consistent discernable effect on gene-functional categories for either brain or liver tissues in the one-year exposure. In the multigenerational assay, the effects of acute (96 h) exposure to RDX were compared in fish reared to the F(2) generation in either control or RDX-exposure conditions. Enrichment of gene functions including neuroexcitatory glutamate metabolism, sensory signaling, and neurological development were observed comparing control-reared and RDX-reared fish. Our results indicated that exposure to RDX at a concentration representing the highest levels observed in the environment (0.83 mg/L) had limited impacts on genomic, individual, and population-level endpoints in fathead minnows in a one-year exposure. However, multigenerational exposures altered transcript expression related to neural development and function. Environ.

Analysis of common and specific mechanisms of liver function affected by nitrotoluene compounds

Background

Nitrotoluenes are widely used chemical manufacturing and munitions applications. This group of chemicals has been shown to cause a range of effects from anemia and hypercholesterolemia to testicular atrophy. We have examined the molecular and functional effects of five different, but structurally related, nitrotoluenes on using an integrative systems biology approach to gain insight into common and disparate mechanisms underlying effects caused by these chemicals.

Methodology/Principal Findings

Sprague-Dawley female rats were exposed via gavage to one of five concentrations of one of five nitrotoluenes [2,4,6-trinitrotoluene (TNT), 2-amino-4,6-dinitrotoluene (2ADNT) 4-amino-2,6-dinitrotoulene (4ADNT), 2,4-dinitrotoluene (2,4DNT) and 2,6-dinitrotoluene (2,6DNT)] with necropsy and tissue collection at 24 or 48 h. Gene expression profile results correlated well with clinical data and liver histopathology that lead to the concept that hematotoxicity was followed by hepatotoxicity. Overall, 2,4DNT, 2,6DNT and TNT had stronger effects than 2ADNT and 4ADNT. Common functional terms, gene expression patterns, pathways and networks were regulated across all nitrotoluenes. These pathways included NRF2-mediated oxidative stress response, aryl hydrocarbon receptor signaling, LPS/IL-1 mediated inhibition of RXR function, xenobiotic metabolism signaling and metabolism of xenobiotics by cytochrome P450. One biological process common to all compounds, lipid metabolism, was found to be impacted both at the transcriptional and lipid production level.

Conclusions/Significance

A systems biology strategy was used to identify biochemical pathways affected by five nitroaromatic compounds and to integrate data that tie biochemical alterations to pathological changes. An integrative graphical network model was constructed by combining genomic, gene pathway, lipidomic, and physiological endpoint results to better understand mechanisms of liver toxicity and physiological endpoints affected by these compounds.

Systems biology: leading the revolution in ecotoxicology

The rapid development of new technologies such as transcriptomics, proteomics, and metabolomics (Omics) are changing the way ecotoxicology is practiced. The data deluge has begun with genomes of over 65 different aquatic species that are currently being sequenced, and many times that number with at least some level of transcriptome sequencing. Integrating these top-down methodologies is an essential task in the field of systems biology. Systems biology is a biology-based interdisciplinary field that focuses on complex interactions in biological systems, with the intent to model and discover emergent properties of the system. Recent studies demonstrate that Omics technologies provide valuable insight into ecotoxicity, both in laboratory exposures with model organisms and with animals exposed in the field. However, these approaches require a context of the whole animal and population to be relevant. Powerful approaches using reverse engineering to determine interacting networks of genes, proteins, or biochemical reactions are uncovering unique responses to toxicants. Modeling efforts in aquatic animals are evolving to interrelate the interacting networks of a system and the flow of information linking these elements. Just as is happening in medicine, systems biology approaches that allow the integration of many different scales of interaction and information are already driving a revolution in understanding the impacts of pollutants on aquatic systems.

Investigations of transcript expression in fathead minnow (Pimephales promelas) brain tissue reveal toxicological impacts of RDX exposure

Production, usage and disposal of the munitions constituent (MC) cyclotrimethylenetrinitramine (RDX) has led to environmental releases on military facilities. The chemical attributes of RDX are conducive for leaching to surface water which may put aquatic organisms at risk of exposure. Because RDX has been observed to cause aberrant neuromuscular effects across a wide range of animal phyla, we assessed the effects of RDX on central nervous system (CNS) functions in the representative aquatic ecotoxicological model species, fathead minnow (Pimephales promelas). We developed a fathead minnow brain-tissue cDNA library enriched for transcripts differentially expressed in response to RDX and trinitrotoluene (TNT) exposure. All 4,128 cDNAs were sequenced, quality filtered and assembled yielding 2230 unique sequences and 945 significant blastx matches (E ≤10(-5)). The cDNA library was leveraged to create custom-spotted microarrays for use in transcript expression assays. The impact of RDX on transcript expression in brain tissue was examined in fathead minnows exposed to RDX at 0.625, 2.5, 5, 10mg/L or an acetone-spike control for 10 days. Overt toxicity of RDX in fathead minnow occurred only at the highest exposure concentration resulting in 50% mortality and weight loss. Conversely, Bayesian analysis of microarray data indicated significant changes in transcript expression at concentrations as low as 0.625 mg/L. In total, 154 cDNAs representing 44 unique transcripts were differentially expressed in RDX exposures, the majority of which were validated by reverse transcriptase-quantitative PCR (RT-qPCR). Investigation of molecular pathways, gene ontology (GO) and individual gene functions affected by RDX exposures indicated changes in metabolic processes involved in: oxygen transport, neurological function, calcium binding/signaling, energy metabolism, cell growth/division, oxidative stress and ubiquitination. In total, our study indicated that RDX exposure affected molecular processes critical to CNS function in fathead minnow.

Reverse engineering adverse outcome pathways

The toxicological effects of many stressors are mediated through unknown, or incompletely characterized, mechanisms of action. The application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) can be used to overcome these limitations. This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows (FHM, Pimephales promelas). Gene expression changes in FHM ovaries in response to seven different chemicals, over different times, doses, and in vivo versus in vitro conditions, were captured in a large data set of 868 arrays. Potential AOPs of the antiandrogen flutamide were examined using two mutual information-based methods to infer gene regulatory networks and potential AOPs. Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment, thus leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biological processes, biomarkers, or alternative endpoints that can be used to monitor an AOP. Finally, the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented.

Predictive minimum description length principle approach to inferring gene regulatory networks

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.