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In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass (Micropterus salmoides)

  • Danilo Basili
    Danilo Basili
    Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
  • Ji-Liang Zhang
    Ji-Liang Zhang
    Henan Open Laboratory of Key Subjects of Environmental and Animal Products Safety, College of Animal Science and Technology, Henan University of Science and Technology, Henan 471003, China
    Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
  • John Herbert
    John Herbert
    Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
    More by John Herbert
  • Kevin Kroll
    Kevin Kroll
    Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
    More by Kevin Kroll
  • Nancy D. Denslow
    Nancy D. Denslow
    Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
  • Christopher J. Martyniuk
    Christopher J. Martyniuk
    Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
  • Francesco Falciani
    Francesco Falciani
    Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
  • , and 
  • Philipp Antczak*
    Philipp Antczak
    Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
    *Phone: +44 151 795 4564; e-mail [email protected] (P.A.).
Cite this: Environ. Sci. Technol. 2018, 52, 13, 7553–7565
Publication Date (Web):June 7, 2018
https://doi.org/10.1021/acs.est.8b02805
Copyright © 2018 American Chemical Society
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Supporting Info (1)»

Abstract

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass (Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.

Introduction

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The increasing amount of pollutants released into the environment is a major issue for the development of a sustainable economy. Growing numbers of anthropogenic pollutants affect freshwater and marine environments with profound impact on species of economic importance. This leads to an increase in the need for additional ecosystem maintenance to secure a constant, minimally burdened, food supply. (1,2) Moreover, due to their position in the food chain, higher level organisms (including humans) can be negatively affected through biomagnification of these toxic substances. (3,4) Endocrine disruptors (EDs), in particular, have the ability to associate with adverse effects, such as reproduction. (5,6) EDs are exogenous agents that interfere with the synthesis, transport, binding action, metabolism, secretion, or elimination of natural endogenous hormones responsible for reproduction, homeostasis, and developmental processes; the disturbance of which may lead to adverse outcomes. (7−9) These compounds are commonly found in daily use products such as detergents, cosmetics, processed food, and products containing flame retardants. (10) Many taxa exhibit reproductive and developmental abnormalities following exposure to EDs, including humans, reptiles, mammals, amphibians, birds, fish, and invertebrate organisms. (11) For example, one of the most common abnormalities in animals in the aquatic environment that is caused by exposure to EDs is intersex, defined as males that have both sperm and oocytes in their testis. (12) EDs elicit tissue-specific responses (13) and have been shown to be toxic even at very low concentrations. (14) Moreover, time of exposure has been shown to be a crucial factor that determines the potency of EDs. (7)
Largemouth bass (LMB) (Micropterus salmoides) is an important economic fish species widely distributed throughout the U.S.A. LMB are popular as a sports fish, and they represent a keystone species in freshwater ecosystems due to their trophic position as an apex predator. LMB reproduction is typically synchronous as they develop their gonads over a spawning season, which is controlled by both environmental and physiological factors (e.g., temperature, photoperiod, and endogenous hormonal triggers). (15) Oocyte growth can be divided into two main stages of development, classified as primary growth or previtellogenic and secondary growth or vitellogenesis. (16,17) These developmental stages can be further divided into discrete reproductive stages depending on ovarian morphology and oocyte maturation as defined by Martyniuk et al. (18,19) Quantifying the molecular events underlying ovary development dynamics in response to pollutants facilitates improved understanding of the mechanisms and hence improves our ability to design and manufacture safer products.
In this study, we used transcriptome profiling data coupled with computational approaches to model the effects of chemicals in the LMB ovary. Using omics data sets, we first constructed a dynamic model representing the development of healthy ovaries from unexposed fish. By mapping the responses of a transcriptome from LMB collected from a polluted site, we were then able to identify modules (clusters of genes), which are perturbed at different stages of ovarian development. By utilizing the Comparative Toxicogenomic Database (CTD), (20) a robust database providing information about chemical interaction with genes, proteins, and disease, we identified tretinoin and quercetin as potential chemicals that were associated with the observed molecular response in LMB ovary following reproductive disruption. Both compounds were subsequently tested for their potential as reproductive endocrine disruptors using exposure experiments in the laboratory with LMB. This study demonstrates that by utilizing computational approaches and online knowledge bases to understand the underlying molecular response of organisms, it is possible to identify putative chemical candidates that may impact reproductive health. This approach is highly relevant for classifying chemicals prior to conducting risk assessments, and we propose that this is a viable approach for chemical prioritization, reducing animal numbers, and developing safer chemicals in the public domain.

Materials and Methods

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Experimental Design

For a detailed description of the experimental design and the data processing workflow see ref (19). Briefly, wild largemouth bass (LMB) were collected from the St. Johns River in Florida from October 2005 to April 2007 at Welaka (29.48° N, 81.67° W) located approximately 20 miles south of Palatka, FL. This area is considered to be relatively free from the influence of industrial effluent and agricultural runoff. (21) Water temperature varies dramatically over the year and is reported on a month to month basis by Martyniuk et al. (18) At the sampling time, ovaries were dissected, and fish were categorized based on histology into 7 different stages of reproductive development: perinucleolar (PN), cortical alveoli (CA), early vitellogenin (eVTG), late vitellogenin (lVTG), early ovarian maturation (eOM), late ovarian maturation (lOM), and ovulation (OV). The first stage of development is named the perinuclear stage (PN), and it is characterized by the formation of the follicle which is made up of granulosa cells surrounding the oocyte, a basal lamina, and the thecal cells. Moreover, meiosis is arrested at the diplotene stage of prophase I and an intensive transcriptional activity is taking place. (22) The name of the stage is given by the production of multiple nucleoli following nucleolar amplification, which becomes oriented in a perinuclear position. (23) The oocyte keeps increasing its size entering in the cortical alveoli stage (CA). This stage is characterized by the formation of large vesicles organized in a multilayered structure at the oocyte periphery that keeps growing and fuses together. (24) The next stage is characterized by the oocyte uptake of nutritional resources as the egg yolk protein vitellogenin (Vtg) and is named vitellogenesis stage (VTG). The Vtg is synthesized by hepatocytes, carried to the oocyte by the bloodstream, and incorporated through a receptor-mediated process. (17,22) Once its uptake is completed the oocyte enters the maturation stage (OM) where the nucleus, also called the germinal vesicle, starts to migrate to the animal pole of the oocyte. At the ovulation stage (OV), the oocyte emerges from the follicle becoming an egg. Physiological end points as Plasma vitellogenin (VTG) levels, 17β-estradiol (E2) levels, and gonadosomatic index (GSI) were also measured. Ovaries were then processed for gene expression profiling, with four biological replicates for each ovarian stage, using a custom LMB microarray platform. (25) These data were used to characterize the molecular events underlying oocyte maturation in the LMB ovary, identifying potential biomarkers of atresia. (19) In a second study, a mesocosm experiment was set up by placing wild adult LMB (+ 3 years) at different stages of reproduction (late CA or eVTG), sampled from DeLeon Springs in Florida, in the Apopka ponds in October (collected from the ponds for samples in January), and in January (collected from the ponds for samples in April). LMB were in the ponds for 4 months. Water parameters in the pond were assessed over a 5-year period and were the following for each month. (1) October; ammonia (mg/L) = 0.750, conductivity = 648.57 ± 87.8, DO = 6.89 ± 3.0, gauge = 3.43 ± 0.04, pH = 7.97 ± 0.23, Secchi = 0.58 ± 0.18, and water temperature (F) 75.6 ± 3.4. (2) January; conductivity = 747.18 ± 56.2, DO = 10.55 ± 1.94, gauge 3.44 ± 0.011, pH 8.16 ± 0.17, Secchi = 0.480, water temperature (F) = 60.32 ± 1.74. (3) April; conductivity = 733.26 ± 34.7, depth of collection 0.81 ± 0.62, DO = 8.34 ± 1.40, gauge = 3.43 ± 0.032, pH = 7.98 ± 0.16, Secchi (F) 0.69, and water temperature (F) = 75.1 ± 1.58. This site is well-known to be impacted by anthropogenic sources. (26) The contaminant load consisted of high levels of organochlorine pesticides (DDT, dieldrin, toxaphene, and others). (26) These organochlorine pesticides, are known to disrupt LMB reproduction due to their estrogenic and antiandrogenic properties. (27−29) Ovaries were then collected and processed for gene expression profiling using four biological replicates. (26)

Annotation

The LMB microarray (Agilent ID: GPL 13229; Santa Clara, CA, U.S.A.) consists of 15 950 sequences. To improve on the previous multispecies annotation, the microarray was reannotated using the most recent annotated genomes available. Two approaches, leveraging the power of the NCBI blast tool, were used: (1) Using blastn (search a nucleotide database using a nucleotide query), we aligned all of the array-design sequences against the most closely related and most completely annotated genome, which was the three-spined stickleback (Gasterosteus aculeatus) and then, using the same method, the sequences were aligned to RefSeq zebrafish (Danio rerio) cDNA; (2) The array-design sequences were compared against the three-spined stickleback protein with blastx (search a protein database using a translated nucleotide query) and then with blastp (search a protein database using a protein query) against the RefSeq zebrafish protein. In both approaches, the e-value threshold was set at 1e–6. The two approaches led to the annotation of 6373 and 5522 sequences, respectively. A total of 7772 genes (4926 unique genes) were successfully associated with an official gene symbol of which 4338 were associated with the zebrafish genome (98%) providing improved coverage over the previous annotation (1031 genes, 15%).

Differential Gene Expression and Clustering

Differentially expressed genes were identified using the Significance Analysis of Microarray (SAM) (30) within the statistical environment R (“samr” package). To identify expression changes across the different stages of development, we applied a time-course SAM. To identify genes whose expression was differentially expressed in at least one stage, we applied a multiclass SAM, which does not consider the time and is not constrained by a specific response function. Significant genes were defined by a threshold of 10% FDR (False Discovery Rate) to maximize the number of differentially expressed genes. To visualize the relationship between samples the differentially expressed genes were used as input to a principal component analysis using the “prcomp” package within the statistical environment R. To simplify the complexity of the data set we set to identify clusters of genes whose expression was correlated across the different stages of development. We employed SOTA (Self-Organizing Tree Algorithm) (31) using a pearson correlation distance measure, an unsupervised neural network with a binary tree topology that can be easily scaled to large data sets. Each cluster was functionally annotated using the web-based software tool DAVID. (32) Biological gene ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways with an FDR < 5% were considered.

Dynamic Modeling and Chemical Mapping

Modules identified by the SOTA approach using the two different SAM methods were then merged into a single data set. We chose to represent each module with a fourth degree polynomial interpolation of its genes. The approach interpolated the 7 ovarian stages to 100 pseudotime points. The resulting data set and the physiological measurements (VTG, GSI, and E2) were then used as input to a TimeDelay-ARACNE (TDA) algorithm. (33) This method extracts dependencies between two genes by incrementally delaying the expression profile of one gene against another; this results in a comparison where the timecourse of gene1t0, t1, t2,...t(n–1)” is then compared to a delayed timecourse of gene2t1, t2, t3,...tn”. The amount by which one profile is delayed with respect to another is defined as the time-delay. By testing multiple time-delays and identifying the highest dependency between two genes, a directionality can be inferred and represented graphically in a network format. TDA has been successfully compared to dynamic Bayesian networks and ordinary differential equations and it has been shown to have a good accuracy for network reconstruction. (33) To identify whether any of our identified clusters were up and down regulated as a result of pollution, we utilized a gene set enrichment analysis (GSEA). (34) In addition to the standard GSEA procedure, a preranked list of genes can be provided to the algorithm. To define our preranked gene list, we conducted a “two-class unpaired” SAM analysis between the polluted and clean sites and recorded the d-statistic for each gene. The d-statistics were then provided to the GSEA approach (to rank each gene). We then imported the previously identified clusters as gene-sets. GSEA then tested whether any of our identified clusters were generally up (positive d-statistic) or down (negative d-statistic) regulated in respect to pollution. Clusters identified as enriched were recorded and visually represented on the network view. Functional enrichment of the subnetwork of interest was achieved using DAVID with a 1% FDR threshold applied.

CTD Enrichment

To identify chemicals with the ability to affect the subnetwork of interest, we utilized the curated data collection in the Comparative Toxicogenomics Database (CTD). (20) The CTD provides manually curated information about interactions between chemical and gene/protein as well as chemical-disease relationships to aid in the development of hypotheses about mechanisms underlying chemical and environmentally influenced disease. To identify which potential compounds might be involved in generating the transcriptome response that we observed, we first downloaded the CTD (Download-date: 14 January 2016) and identified the species with the broadest number of chemical interactions (Homo sapiens). As the zebrafish has been extensively used as a model system for human disease, human gene ortholog information is better defined than other species covered in the CTD. We selected the human subset of data and although this is a needed compromise, it provides us with the opportunity to explore potentially conserved mechanisms. We first converted our zebrafish genes to human genes using ZebrafishMine (35) (3977 genes).To identify which chemical may interact with the subnetwork of interest we calculated the EASE score (Expression Analysis Systematic Explorer; as defined in the DAVID web-based tool (32)) against the CTD database filtered for those compounds having associated more than 5 genes. The thresholding is necessary to reduce the potential of identifying spurious compound hits where a low gene-set size results in a significant p-value. Retrieved p-values were adjusted using a Benjamini and Hochberg correction (analogous to a FDR). This resulted in a list of compounds that have more genes in common than expected by random chance. We ordered the compounds by their respective p-value (10% FDR threshold) to identify the top identified toxicants.

Prediction Validation

Twenty reproductive largemouth bass (10 males and 10 females for each exposure group) were injected intraperitoneally with 10 or 100 μg/kg of quercetin or tretinoin dissolved in DMSO. The controls were injected with the carrier solution DMSO. Following a 48 h exposure, the fish were anesthetized, weighed, bled, and dissected. For real-time PCR, sample sizes for female ovary and liver were as follows: Control (n = 6), Quer 10 (n = 6), Quer 100 (n = 7), Tret 10 (n = 6), and Tret 100 (n = 6). Sample sizes for male liver were as follows: Control (n = 6), Quer 10 (n = 6), Quer 100 (n = 6), Tret 10 (n = 6), and Tret 100 (n = 6). Gonad and liver tissues were collected and flash frozen in liquid nitrogen for RNA purification and extraction. RNA was assessed for quality using the Agilent 2100 Bioanalyzer and all samples showed a RIN > 7.0. RNA was subjected to a DNase treatment using DNase Turbo as per manufacturer’s protocol (Ambion). The cDNA synthesis was performed using 1 μg total RNA (using the iScript BioRad protocol). Primer sets for target genes were collected from the literature for LMB. The genes investigated in this study included androgen receptor (ar), estrogen receptor alpha, betaa and betab (erα, erβa, erβb), aromatase (cyp19a), steroidogenic acute regulatory protein (star), vitellogenin (vtg), and vitellogenin receptor (vtgr). We chose these reproductive transcripts because (1) the computational analysis was done in reproductive tissues (liver and ovary) over a breeding season and these transcripts are sensitive to maturation, (2) we have observed that these genes are perturbed by chemicals in LMB, and (3) these gene assays are widely used and reliable in our laboratory. 18S rRNA was used to normalize gene targets. Real-time PCR was performed using the CFX Connect Real-Time PCR Detection System (BioRad) with SSoFast EvaGreen Supermix (BioRad, Hercules, CA, U.S.A.), 200 nM of each forward and reverse primer, and 3.33 μL of diluted cDNA. The two-step thermal cycling parameters were as follows: initial 1-cycle Taq activation at 95 °C for 30 s, followed by 95 °C for 5 s, and primer annealing for 5 s. After 40 cycles, a dissociation curve was generated, starting at 65.0 and ending at 95.0 °C, with increments of 0.5 °C every 5 s. Normalized gene expression was extracted using CFX Manager software with the relative ΔΔCq method (baseline subtracted). All primers used in the qPCR analysis amplified one product, indicated by a single melt curve. Details about the primers are provided in Table S1 of the Supporting Information (SI). Significant differences between group means were analyzed using analysis of variance (ANOVA) followed by Dunn’s posthoc test. A value of p < 0.05 was used to indicate significant differences.

Chemical-Set Enrichment Analysis

To test whether our approach preferentially identifies endocrine disruptors as by random chance we first downloaded a defined list of endocrine disruptors from the CTD. Second, we extracted the estimates from the fisher exact test performed during identification of chemicals. We then used these two data sets as input to a standard Pre-Ranked GSEA.

Results

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Analysis Strategy

The overarching objective of our analysis was to identify chemicals that were most likely to disrupt ovarian development in largemouth bass. This was achieved by developing a dynamic network model, linking changes in the transcriptional state of different stages of normal LMB ovaries and measured key physiological end points (GSI, VTG, and E2). The first step in developing this model was to reduce the overall complexity of the gene expression profiles. We first identified differentially expressed genes during ovary development (Figure 1, step 1a-1b) and then clustered the transcripts, via self-organizing trees, based on similarity of gene expression profiles (Figure 1, step 1c). This reduced the total number of variables in the dynamic network model and provided means for better biological interpretation. The linkages between the key physiological indicators and the clusters of gene expression profiles were inferred using a time-delay mutual information algorithm (Figure 1, step 2), and the response of the transcriptome to the polluted environment was then mapped via gene set enrichment analysis onto the network (Figure 1, step 3). By identifying which gene clusters are (1) directly connected to key physiological end points and (2) significantly enriched in the polluted site, we identified a subnetwork of interest. Finally, by interrogating the CTD, and matching gene expression profiles to the clusters perturbed in an environment under chemical stress, we were able to identify chemical candidates that were predicted to interfere with ovary development (Figure 1, step 4). Resulting chemical stressors were then ordered and the likely candidates tested on their ability to perturb ovary related systems (Figure 1, step 5).

Figure 1

Figure 1. Schematic representation of the analysis pipeline. The data acquired by expression profiling underwent analysis to identify differentially expressed genes (step 1a and b), in addition to clustering genes sharing similar expression profiles (step 1c). The linkage between gene clusters and key physiological indicators such as VTG, GSI, and E2 across the different stages of development were inferred using a mutual information-based algorithm and response of the transcriptome in fish inhabiting a polluted environment were mapped via GSEA (step 2 and 3); the CTD database was interrogated to identify potential chemical candidates with the ability to affect endocrine functions driving ovary development (step 4); and finally, candidate chemicals were then tested experimentally (step 5).

Differential Gene Expression Analysis Identifies Biological Functions Involved in Ovary Development

To understand what biological processes are linked to ovary development we first set to identify genes differentially expressed across the seven stages of development. This was achieved by two criteria: (1) genes which changed expression over the different stages of ovary development (one class SAM time-course), and (2) genes which were significantly different in at least one developmental stage (multiclass). We identified 3057 genes associated with oocyte development and 5047 whose expression was significantly different in at least one stage at 10% FDR (Figure S1). The two gene-lists were then combined to give the best possible understanding of the molecular response to normal ovary development. To visually represent the dynamics of change in the transcriptional state of healthy ovaries, the resulting list of 5199 genes was used as input to a principal component analysis (PCA) (Figure 2). We could project the state of ovary samples in two dimensions while retaining 55% of variance. This visual representation was consistent with a high reproducibility of replicated samples as well as an expected progression from early stages such as perinuclear (PN) to ovary maturation (OV). For each of the gene-sets, a self-organizing tree algorithm (SOTA) was applied and for each cluster, functional annotation was retrieved. This approach yielded 12 and 9 clusters for the time-course and multiclass gene-sets, respectively. Heat-maps of the clusters showed a variation in the responses that were identified by either of the differential gene expression approaches (Figure 3). The identified functions represented were mainly linked to transcriptional and translational activity, energy metabolism, and cell growth activity, for example as mTOR signaling pathway or cell cycle, respectively. Interestingly, mTOR is regulating both cell cycle and energy metabolism by controlling the selecting translation of growth factor induced genes. (36)

Figure 2

Figure 2. Principal component analysis (PCA) shows a clear progression of ovary development from the first to the terminal stage of ovarian development. Stages are defined as PN (perinuclear), CA (cortical alveoli), eVTG (early vitellogenesis), lVTG (late vitellogenesis), eOM (early ovarian maturation), lOM (late ovarian maturation), and OV (ovulation).

Figure 3

Figure 3. Expression profiles of transcripts identified by two different differential gene expression approaches. Red and green show up and down regulation, respectively. Functional annotation is reported with black, red, and blue terms representing gene ontology terms at 5%, 10%, and 20% FDR, respectively.

Dynamic Model of Ovary Development

To develop a dynamic model of ovary development, the identified clusters and key physiological end points were used as a input into TimeDelay-ARACNE (TDA). This resulted in a directed network where edges, linkages between nodes (clusters of genes and end points), indicate a positive or negative direction of effect based on their temporal profiles. The resulting network represented 20 modules linked through 30 edges that are colored red and green to distinguish positive and negative effects (Figure S2). Noteworthy was that several clusters were directly associated with the key physiological end points (Table S2). Cluster 12 was linked to GSI and E2 and contained genes that had representative functions including transduction (ribosome and spliceosome), nucleotide metabolism (purine and pyrimidine metabolism), and energy metabolism (oxidative phosphorylation). Cluster 6 linked to E2 and contained genes that had representative functions that were associated with glycolysis/gluconeogenesis pathway. Functions associated with embryo development (eye development), DNA replication (DNA replication, nucleotide excision repair, and mismatch repair), translational activity (ribosome), and energy metabolism (oxidative phosphorylation) were found to be represented in cluster 10 which was linked to VTG.

Mapping the Effects of Pollutant Exposure on the Model for Healthy Ovary Development

Having developed the network representing normal ovary development, we hypothesized that this would provide a platform for quantifying the effects of pollution. To test this hypothesis, we utilized an additional data set, which was developed as part of the same initial study, which compared ovary transcriptomes of LMB collected from a heavily polluted and a pristine site. All fish in this experiment were histologically classified as late vitellogenesis stage and so the molecular differences were expected to affect clusters around the level of eVTG-lVTG. By using gene set enrichment analysis, we were able to determine whether any of the clusters were either up or down-regulated as a result of the polluted environment. Interestingly, the effect of pollution extended well beyond the expected stage of development, showing significant effects even in clusters placed in PN and CA stages suggesting a regression, developmental inhibition, or a stronger overlap of different ovary development stages appearing simultaneously during the process (Figure 4). This led to the identification of a subnetwork, which connected to physiological end points and clusters 2, 6, 7, 10, 12, and 17. Functional characterization of this subnetwork revealed evidence of E2-dependent functions, which are well-known to drive ovary development (Table S3).

Figure 4

Figure 4. Transcriptome responses in the ovary due to a polluted site were mapped onto the developed dynamic network and a subnetwork of interest, which included all three physiological measurements, was identified. Clusters positively or negatively enriched with pollution-related genes are displayed in red and green, respectively.

Identification of Chemicals with the Potential to Disrupt Ovarian Development

Having identified a subnetwork with evidence for ovary development perturbation, we next asked the question of whether it was possible to identify chemical compounds that have the ability to perturb these functions. We therefore collapsed the clusters (2, 6, 7, 10, 12, and 17) within our subnetwork to a single entity and interrogated the CTD database for potential entries of interest that have been shown to perturb this set of genes. This resulted in a list of 10 entries, cyclosporine, valproic acid, copper sulfate, methyl methanesulfonate, cobalthous chloride, acetaminophen, atrazine, formaldheyde, tretinoin, and quercetin, that showed a significant enrichment with the potential to perturb networks associated with ovary development (Table 1). Some of the identified entries are widely used as drugs (valproic acid, cyclosporine, and tretinoin) or food additives (quercetin). interestingly, three of the identified compounds (valproic acid, cyclosporine, and quercetin) have already been shown to have estrogenic activity which increased our confidence that our approach identified likely candidates for endocrine disruption activity.
Table 1. List of Chemical Compounds Identified Using the CTD Databasea
chemicaldescriptionendocrine referenceFDR
valproic acidDrug used to treat epilepsy and bipolar disorders. It acts as histone deacetylase, by blocking voltage-gated sodium channels or affecting GABA levels.estrogenic activity (37)8.9 × 10–34
steroidogenic effect (38)
cyclosporineImmunosuppressant drug used to reduce the activity of the immune system by interfering with the activity and growth of T cells.estrogenic effect (39)9.9 × 10–31
copper sulfateInorganic compound with a wide range of application. It is mainly used by industries or as analytical reagent and is environmentally relevant.steroidogenic inhibition (40)3.3 × 10–21
methyl methanesulfonateAlkylating agent used in cancer treatmentreproductive toxicity (41)1.0 × 10–13
cobaltous chlorideInorganic compound with a wide range of application.indirect antiestrogenic (42)1.7 × 10–12
acetaminophenDrug commonly known as Paracetamol, it is used to treat pain and feverantiestrogenic activity (43)1.7 × 10–11
antiandrogenic activity (44)
atrazineHerbicide used to prevent broadleaf weed in cropsantiandrogenic and antiestrogenic (45)1.2 × 10–11
quercetinFlavonoid found in many fruits, vegetables, leaves and grains used as dietary supplement.estrogenic activity (46)1.6 × 10–11
formaldehydeOrganic compound used for the production of resins and it is known to be carcinogen.reproductive toxicity (47)2.3 × 10–11
tretinoinA retinoic acid used to treat acne and leukemia. It acts by forcing APL cells to differentiate and stops them from proliferating. 6.6 × 10–10
a

These compounds are predicted to affect biological functions underlying the gene regulatory network of ovarian development. Compound description and endocrine references are reported along with p-values of enrichment.

To identify the best possible candidates for further testing, we compared each of the 6 compounds effects to well-known key endocrine-related genes (erα, erβ(α+b), star, ctsD, ctsB, fst, cyp19a, cyp3a, nr0b1, zp3) (Table S4). This identified cyclosporine, acetaminophen, atrazine, tretinoin, quercetin, and valproic acid as chemicals likely to have the potential to disrupt endocrine functions. As all of these chemicals except tretinoin have been previously shown to exhibit endocrine or reproductive toxicity-related effects, and quercetin has been shown to impact mammalian ovary development, (46) we then experimentally tested these two compounds as endocrine disruptors in LMB.

Experimental Validation of Predicted Compounds and Their Effects on Endocrine Related Genes

To assess the potential of the two selected compounds, quercetin and tretinoin, to perturb ovary development, fish were exposed at 10 μg/kg and 100 μg/kg of each compound for 48 h. The expression of key endocrine genes, androgen receptor (ar), the three estrogen receptors (erα, erβa and erβb), aromatase (cyp19a), the steroidogenic acute regulatory protein (star), and the vitellogenin receptor (vtgr) were tested by qPCR in ovary and erα and vtg in liver tissue. Neither of the compounds significantly affected the expression of androgen receptor in ovary tissue (Figure 5). Quercetin significantly perturbed all three ERs at the highest dose examined (erβa was significant also at the lowest dose) while tretinoin perturbed erβa only at the high dose. erβa, in particular, appeared to be more responsive to the treatments than the other two receptors. The high dose of quercetin also perturbed cyp19a and vtgr expression while tretinoin affected star expression at 10 μg/kg and vtgr expression at 100 μg/kg. In the liver, the low dose of tretinoin only significantly affected the expression levels of the erα in males while vtg was not affected by either of the two compounds (Figure 6). To verify that our method preferentially identifies endocrine disruptors we applied a GSEA algorithm with the EDCs, as defined by the CTD, as the set. The EDC chemical set was found to be positively enriched (FDR < 5%) adding more confidence to our approach (Figure S3).

Figure 5

Figure 5. Expression levels of reproductive-related transcripts in ovary tissue after exposure to candidate chemicals.

Figure 6

Figure 6. Expression levels of reproductive-related transcripts in liver tissue after exposure to candidate chemicals in both males and females.

Discussion

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Gene Regulatory Network of Ovary Development

Previously, we have characterized molecular pathways and temporal gene expression patterns in female largemouth bass across the different stages of ovarian development. (19) Here, we identified relationships between gene expression patterns in the different stages of ovarian development. This is the first study of its kind where a dynamic model is able to link gene expression patterns at early stages of development with those at later stages. Remarkably, we demonstrated that this approach can be used to identify chemicals that alter endocrine-related functions driving ovary development.
Our first goal was to develop a dynamic model of healthy ovary development that functionally describes the dynamics undergoing ovarian maturation and oocyte growth. The approach we choose is entirely data driven. Unlike the currently available models that are based on the description of the pharmacodynamics underlying hormones and VTG activity along the hypothalamic–pituitary–gonadal (HPG) axis, (48−51) our model is able to capture completely novel regulatory interactions. We successfully identified clusters of genes whose expression was related to a particular stage of ovarian development. Previous studies aiming to characterize gene expression profiles underlying ovarian development have already been performed in different fish species, and these are consistent with our model. Guzman et al. (52) characterized expression profiles of ovarian genes regulated by the follicle-stimulating hormone (fsh) in Coho salmon (Oncorhynchus kisutch). They determined that the expression of the transcript of cyp19a (aromatase) peaked at eVTG-lVTG stage and showed a positive correlation with the transcript of the fsh receptor (fshr), supporting the idea that fsh stimulates the production of E2 in the ovary via upregulation of cyp19a. In our network model, the cyp19a belongs to cluster 10, which has the initial change of expression at the eVTG-lVTG stage, supporting similar relationships as identified by Guzman et al. Gene expression profiles during vitellogenesis were also determined in Atlantic cod (Gadus morhua) by Breton et al. (53) They identified cyp19a as overexpressed (4-fold) during vitellogenesis in the ovary, which drives the synthesis of E2 that ultimately regulates vtg synthesis in the liver. In our network model, aromatase expression is included in cluster 10, which is connected with VTG, supporting the data presented by Breton and collaborators. Here we also infer a gene regulatory network linking these profiles with key physiological end points. Moreover, the ability of either a physiological end point or a cluster at an early stage of development to trigger the activation or inhibition of features at a later stage of development was identified by using the temporal profile of ovary progression. Gene regulatory networks are particularly useful in the field of environmental toxicology for identifying chemical mode of action, (54) deriving toxicity thresholds (55) and for inferring gene targets of drugs and chemical compounds. (56) The gene regulatory network we developed precisely aids in the prediction of gene targets of chemical compounds that can be used potentially to develop a new set of biomarkers of ovarian toxicity.
One limitation of our model is that it does not consider all of the components of the HPG axis, we therefore may miss components/interactions of the system critical to ovary development. However, our simplification does consider time delays associated with signal transduction processes, leading to the development of a model where E2 and VTG are key drivers of ovarian development as they show time sensitivity in reproduction. Development of more detailed and more biologically accurate mathematical models is required to better understand the full scope of effects that stressors are able to trigger. (57,58)

Tretinoin and Quercetin as Endocrine Disruptors

Chemicals often used as drugs or food additives are widespread in the environment and many of them have already been characterized for their ability to disrupt fundamental biological processes in environmentally relevant species. (59,60) Our computational approach led to the prediction of tretinoin and quercetin as potential endocrine disruptors (EDs) with the ability to alter key biological processes involved in the ovary development of the LMB.
Our results suggest quercetin has the ability to disrupt reproductive targets such as the estrogen receptors, the aromatase (cyp19a) and the vtgr in the ovary. Quercetin is a natural occurring flavonoid found in many fruits, vegetables, leaves, and grains. Flavonoids, in plants, have many different roles as they improve growth and seedlings development, attract pollinators helping seed germination, and are responsible for the aroma and the colors of flowers. (61) Moreover, they serve as a barriers against many environmental stresses such as UV radiation. (62) Quercetin has been demonstrated to be beneficial to health in mammals because of its antioxidative, anticancer, free radical scavenging, and antiviral activities. (63,64) Its presence in the environment can be associated with industrial effluents from the pulp and paper industry processing plant material. (65,66) Quercetin is relevant for aquaculture as it has been investigated as a potential fish food supplement for its beneficial properties. (67) Moreover, it also has potential effects in lowering levels of lipids (68−70) whose blood presence in fish has been associated with declining health conditions. (71) Few studies have also demonstrated that quercetin may improve follicular development and oocyte quality in vitro and in vivo. (72,73) Our findings suggest that low amounts of quercetin only affect the expression of erβa. However, quercetin may also have negative effects on fish as shown by Weber et al., which demonstrated that quercetin exposure (100 ppb) in female Japanese medaka (Oryzias latipes) promoted follicular atresia. (74) Further studies have also shown that quercetin has estrogenic-like effects on ovary development. (46)
Further experimentally evidence for the potential of tretinoin to act on key endocrine genes was conducted and demonstrated its effect on erβa, vtgr, and star. Tretinoin, also called all-trans retinoic acid (ATRA), is one of the metabolites of vitamin A (retinol). Retinoic acid is a biologically active metabolite of vitamin A (retinol) that, through the binding with retinoic acid receptors (RARs and RXRs), is involved in a wide range of biological functions including embryo development, (75,76) immune system, (77) reproduction, (78,79) and vision system. (80) Although tretinoin mechanism of action is unknown, it may elicit its molecular action through the activation of retinoid receptors (81) as well as PPAR. (82) Tretinoin is a drug used worldwide for the treatment of acne vulgaris and photodamage. (81) Despite its common use, few studies have been conducted to address any potential environmental toxicity, and most of these studies reveal developmental toxicity. (83−85) The only study which investigated potential effects of tretinoin on ovarian developmental processes was carried out by Pu et al., which showed that ATRA improved in vitro oocyte nuclear maturation in goat after a 22 h exposure at concentrations below the ones tested in this publication. (86) Despite the fact that this compound is not classified as a chemical of concern for the environment, it nevertheless demonstrates that our approach can predict chemicals de novo as potential reproductive disruptors.
The additional experimental test performed have revealed new insight into the toxicity of tretinoin and quercetin, supporting predictions that these compounds can act as EDs based upon changes in mRNA levels for estrogen receptors, aromatase, star and vtgr. Interestingly, the two compounds seemed to act differently. Expression levels of all estrogen receptors were affected by quercetin. It is interesting to notice the nonmonotonic dose–response behavior of tretinoin for star expression levels which has been previously observed in ED compounds. (87) However, further experimental validation is required to understand the relationship between tretinoin and this endocrine disruption potential. For the first time, we provide evidence that tretinoin can affect transcripts related to steroidogenesis and vtgr mRNA levels. Evidence of quercetin ovarian toxicity was also associated with aromatase activity. This demonstrates that omics analyses of target-organ specific perturbation can identify highly relevant toxicants that have yet to be tested. Validation of our predictions further increase our confidence that our findings have the potential to improve environmental risk assessment as well as providing a new tool for screening chemical compounds.

Supporting Information

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. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b02805.

  • More detailed information on the utilized Primers for qPCR testing of tretinoin and quercetin; Venn Diagrams depicting the similarity between the two differential gene expression analyses performed; a network depicting the dynamic relationships between clusters; tables showing the functional enrichment of gene clusters as well as functional enrichment of the identified subnetwork; selection criteria for compounds for experimental validation; GSEA analysis of the ability of our method to select endocrine disrupting chemicals; and additional references (PDF)

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Author Information

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  • Corresponding Author
  • Authors
    • Danilo Basili - Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
    • Ji-Liang Zhang - Henan Open Laboratory of Key Subjects of Environmental and Animal Products Safety, College of Animal Science and Technology, Henan University of Science and Technology, Henan 471003, ChinaCenter for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
    • John Herbert - Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
    • Kevin Kroll - Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
    • Nancy D. Denslow - Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
    • Christopher J. Martyniuk - Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine and UF Genetics Institute, University of Florida, Gainesville, Florida 32611, United States
    • Francesco Falciani - Institute for Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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The authors acknowledges support from the Research Councils UK under Grant No. NE/M01939X/1.

References

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  • Abstract

    Figure 1

    Figure 1. Schematic representation of the analysis pipeline. The data acquired by expression profiling underwent analysis to identify differentially expressed genes (step 1a and b), in addition to clustering genes sharing similar expression profiles (step 1c). The linkage between gene clusters and key physiological indicators such as VTG, GSI, and E2 across the different stages of development were inferred using a mutual information-based algorithm and response of the transcriptome in fish inhabiting a polluted environment were mapped via GSEA (step 2 and 3); the CTD database was interrogated to identify potential chemical candidates with the ability to affect endocrine functions driving ovary development (step 4); and finally, candidate chemicals were then tested experimentally (step 5).

    Figure 2

    Figure 2. Principal component analysis (PCA) shows a clear progression of ovary development from the first to the terminal stage of ovarian development. Stages are defined as PN (perinuclear), CA (cortical alveoli), eVTG (early vitellogenesis), lVTG (late vitellogenesis), eOM (early ovarian maturation), lOM (late ovarian maturation), and OV (ovulation).

    Figure 3

    Figure 3. Expression profiles of transcripts identified by two different differential gene expression approaches. Red and green show up and down regulation, respectively. Functional annotation is reported with black, red, and blue terms representing gene ontology terms at 5%, 10%, and 20% FDR, respectively.

    Figure 4

    Figure 4. Transcriptome responses in the ovary due to a polluted site were mapped onto the developed dynamic network and a subnetwork of interest, which included all three physiological measurements, was identified. Clusters positively or negatively enriched with pollution-related genes are displayed in red and green, respectively.

    Figure 5

    Figure 5. Expression levels of reproductive-related transcripts in ovary tissue after exposure to candidate chemicals.

    Figure 6

    Figure 6. Expression levels of reproductive-related transcripts in liver tissue after exposure to candidate chemicals in both males and females.

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    • More detailed information on the utilized Primers for qPCR testing of tretinoin and quercetin; Venn Diagrams depicting the similarity between the two differential gene expression analyses performed; a network depicting the dynamic relationships between clusters; tables showing the functional enrichment of gene clusters as well as functional enrichment of the identified subnetwork; selection criteria for compounds for experimental validation; GSEA analysis of the ability of our method to select endocrine disrupting chemicals; and additional references (PDF)


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