Mechanistic Computational Model of Ovarian Steroidogenesis to Predict Biochemical Responses to Endocrine Active Compounds

Reaction thermodynamics as a constraint on piscine steroidogenesis flux distributions

While a considerable amount is known of the dynamics of piscine steroidogenesis during reproduction, the influence of thermodynamics constraints on its control has not been studied. In this manuscript, Gibbs free energy change of reactions was calculated for piscine steroidogenesis using the in silico eQuilibrator thermodynamics calculator. The analysis identified cytochrome P450 (cyp450) oxidoreductase reactions to have more negative Gibbs free energy changes relative to hydroxysteroid (HSD) and transferase reactions. In addition, a more favorable Gibbs free energy change was predicted for the Δ5 (cyp450 catalyzed) vs. Δ4 (HSD catalyzed) steroidogenesis branch-point, which converts pregnenolone to 17α-hydroxypregnenolone or progesterone respectively. Comparison of in silico predictions with in vivo experimentally measured flux across the Δ5 vs. Δ4 branch-point showed higher flux through the thermodynamically more favorable Δ5 pathway in reproducing or spawning vs. non-spawning fathead minnows (Pimephales promelas). However, the exposure of fish to endocrine stressors such as hypoxia or the synthetic estrogen 17α-ethinylestradiol (EE2), resulted in increased flux through both Δ5 and Δ4 pathways, indicating an adaptive response to increase steroidogenic redundancy. The correspondence of elevated flux through the Δ5 branch-point in spawning fish indicated the use of a thermodynamically favorable pathway to optimize steroid hormone productions during reproduction. We hypothesize that such selective use of a thermodynamically favorable steroidogenesis pathway may conserve reduced equivalents or transcriptional costs for investment to other biosynthetic or catabolic reactions to support reproduction. If generalizable, such an approach can provide novel insights into the structural principles and regulation of steroidogenesis or other metabolic pathways.

Reduced model for female endocrine dynamics: Validation and functional variations

A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype-displaying relatively normal hormone levels and cycle dynamics-that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone.

Computational modelling of the Δ4 and Δ5 adrenal steroidogenic pathways provides insight into hypocortisolism

This study demonstrates the application of a mathematical steroidogenic model, constructed with individual in vitro enzyme characterisations, to simulate in vivo steroidogenesis in a diseased state. This modelling approach was applied to the South African Angora goat, that suffers from hypocortisolism caused by altered adrenal function. These animals are extremely vulnerable to cold stress, leading to substantial monetary loss in the mohair industry. The Angora goat has increased CYP17A1 17,20-lyase enzyme activity in comparison with hardy livestock species. Determining the effect of this altered adrenal function on adrenal steroidogenesis during a cold stress response is difficult. We developed a model describing adrenal steroidogenesis under control conditions, and under altered steroidogenic conditions where the animal suffers from hypocortisolism. The model is parameterised with experimental data from in vitro enzyme characterisations of a hardy control species. The increased 17,20-lyase activity of the Angora goat CYP17A1 enzyme was subsequently incorporated into the model and the response to physiological stress is simulated under both control and altered adrenal steroidogenic conditions.

Mathematical modeling approaches of cellular endocrinology within the hypothalamo-pituitary-gonadal axis

The reproductive neuroendocrine axis, or hypothalamo-pituitary-gonadal (HPG) axis, is a paragon of complex biological system involving numerous cell types, spread over several anatomical levels communicating through entangled endocrine feedback loops. The HPG axis exhibits remarkable dynamic behaviors on multiple time and space scales, which are an inexhaustible source of studies for mathematical and computational biology. In this review, we will describe a variety of modeling approaches of the HPG axis from a cellular endocrinology viewpoint. We will in particular investigate the questions raised by some of the most striking features of the HPG axis: (i) the pulsatile secretion of hypothalamic and pituitary hormones, and its counterpart, the cell signaling induced by frequency-encoded hormonal signals, and (ii) the dual, gametogenic and glandular function of the gonads, which relies on the tight control of the somatic cell populations ensuring the proper maturation and timely release of the germ cells.

In silico predicted transcriptional regulatory control of steroidogenesis in spawning female fathead minnows (Pimephales promelas)

Oocyte development and maturation (or oogenesis) in spawning female fish is mediated by interrelated transcriptional regulatory and steroidogenesis networks. This study integrates a transcriptional regulatory network (TRN) model of steroidogenic enzyme gene expressions with a flux balance analysis (FBA) model of steroidogenesis. The two models were functionally related. Output from the TRN model (as magnitude gene expression simulated using extreme pathway (ExPa) analysis) was used to re-constrain linear inequality bounds for reactions in the FBA model. This allowed TRN model predictions to impact the steroidogenesis FBA model. These two interrelated models were tested as follows: First, in silico targeted steroidogenic enzyme gene activations in the TRN model showed high co-regulation (67-83%) for genes involved with oocyte growth and development (cyp11a1, cyp17-17,20-lyase, 3β-HSD and cyp19a1a). Whereas, no or low co-regulation corresponded with genes concertedly involved with oocyte final maturation prior to spawning (cyp17-17α-hydroxylase (0%) and 20β-HSD (33%)). Analysis (using FBA) of accompanying steroidogenesis fluxes showed high overlap for enzymes involved with oocyte growth and development versus those involved with final maturation and spawning. Second, the TRN model was parameterized with in vivo changes in the presence/absence of transcription factors (TFs) during oogenesis in female fathead minnows (Pimephales promelas). Oogenesis stages studied included: PreVitellogenic-Vitellogenic, Vitellogenic-Mature, Mature-Ovulated and Ovulated-Atretic stages. Predictions of TRN genes active during oogenesis showed overall elevated expressions for most genes during early oocyte development (PreVitellogenic-Vitellogenic, Vitellogenic-Mature) and post-ovulation (Ovulated-Atretic). Whereas ovulation (Mature-Ovulated) showed highest expression for cyp17-17α-hydroxylase only. FBA showed steroid hormone productions to also follow trends concomitant with steroidogenic enzyme gene expressions. General trends predicted by in silico modeling were similar to those observed in vivo. The integrated computational framework presented was capable of mechanistically representing aspects of reproductive function in fish. This approach can be extended to study reproductive effects under exposure to adverse environmental or anthropogenic stressors.

Steroidogenic abnormalities in translocator protein knockout mice and significance in the aging male

The translocator protein (TSPO) has been proposed to act as a key component in a complex important for mitochondrial cholesterol importation, which is the rate-limiting step in steroid hormone synthesis. However, TSPO function in steroidogenesis has recently been challenged by the development of TSPO knockout (TSPO-KO) mice, as they exhibit normal baseline gonadal testosterone and adrenal corticosteroid production. Here, we demonstrate that despite normal androgen levels in young male TSPO-KO mice, TSPO deficiency alters steroidogenic flux and results in reduced total steroidogenic output. Specific reductions in the levels of progesterone and corticosterone as well as age-dependent androgen deficiency were observed in both young and aged male TSPO-KO mice. Collectively, these findings indicate that while TSPO is not critical for achieving baseline testicular and adrenal steroidogenesis, either indirect effects of TSPO on steroidogenic processes, or compensatory mechanisms and functional redundancy, lead to subtle steroidogenic abnormalities which become exacerbated with aging.

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

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

A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. In recent years there has been rapid growth in understanding fish reproductive biology, which has been motivated in part by recognition of the potential effects that climate change, habitat destruction and contaminant exposure can have on natural and cultured fish populations. New approaches to understanding the impacts of these stressors are being developed that require a systems biology approach with more biologically accurate and detailed mathematical models. We have developed a multi-scale mathematical model of the female rainbow trout hypothalamus-pituitary-ovary-liver axis to use as a tool to help understand the functioning of the system and for extrapolation of laboratory findings of stressor impacts on specific components of the axis. The model describes the essential endocrine components of the female rainbow trout reproductive axis. The model also describes the stage specific growth of maturing oocytes within the ovary and permits the presence of sub-populations of oocytes at different stages of development. Model formulation and parametrization was largely based on previously published in vivo and in vitro data in rainbow trout and new data on the synthesis of gonadotropins in the pituitary. Model predictions were validated against several previously published data sets for annual changes in gonadotropins and estradiol in rainbow trout. Estimates of select model parameters can be obtained from in vitro assays using either quantitative (direct estimation of rate constants) or qualitative (relative change from control values) approaches. This is an important aspect of mathematical models as in vitro, cell-based assays are expected to provide the bulk of experimental data for future risk assessments and will require quantitative physiological models to extrapolate across biological scales.

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. Improving the ability to estimate reproductive performance in fish is important, due to the growth of the aquaculture industry and the need to maintain adequate broodstock and concerns over the effects of anthropogenic stressors on feral fish populations. We present here a quantitative, mathematical model of the female rainbow trout reproductive cycle. We show how the model is able to accurately describe experimentally measured data associated with pituitary, ovarian and liver reproductive performance. We also use the model to describe similar data sets collected in rainbow trout by other researchers. An important value of quantitative biological models is the ability to simulate various physiological conditions, real or hypothetical. We demonstrate this by predicting the effects of exposure to an endocrine disruptor on oocyte growth. The need to limit cost and animal usage will encourage future experimental studies to use in vitro methods. The model presented here can assist with the extrapolation of in vitro effects to the whole fish.

In Silico analysis of perturbed steroidogenesis and gonad growth in fathead minnows (P. promelas) exposed to 17α-ethynylestradiol

The multi-factorial nature of adverse reproductive effects mediated by endocrine disrupting compounds (or EDCs) makes understanding the mechanistic basis of reproductive dysfunction a highly pertinent area of research. As a consequence, a main motivator for continued research is to integrate 'multi-leveled' complexity (i.e., from genes to phenotype) using mathematical methods capable of encapsulating properties of physiological relevance. In this study, an in silico stoichiometric model of piscine steroidogenesis was augmented with a 'biomass' reaction associating the underlying stoichiometry of steroidogenesis with a reaction representative of gonad growth. The ability of the in silico model to predict perturbed steroidogenesis and subsequent effects on gonad growth was tested by exposing reproductively active male and female fathead minnows (Pimephales promelas) to 88 ng/L of the synthetic estrogen, 17α-ethynylestradiol (EE2). The in silico model was parameterized (or constrained) with experimentally quantified concentrations of selected steroid hormones (using mass spectrometry) and fold changes in gene expression (using RT-qPCR) for selected steroidogenic enzyme genes, in gonads of male and female fish. Once constrained, the optimization framework of flux balance analysis (FBA) was used to calculate an optimal flux through the biomass reaction (analogous to gonad growth) and associated steroidogenic flux distributions required to generate biomass. FBA successfully predicted effects of EE2 exposure on fathead minnow gonad growth (%gonadosomatic index or %GSI) and perturbed production of steroid hormones. Specifically, FBA accurately predicted no effects of exposure on male %GSI and a significant reduction for female %GSI. Furthermore, in silico simulations accurately identified disrupted reaction fluxes catalyzing productions of androgens (in male fish) and progestogens (in female fish), an observation which agreed with in vivo experimentation. The analyses presented is the first-ever to successfully associate underlying flux properties of the steroidogenic network with gonad growth in fish, an approach which can incorporate in silico predictions with toxicological risk assessments.

Effects of progesterone and norethindrone on female fathead minnow (Pimephales promelas) steroidogenesis

As knowledge of contaminants capable of adversely modulating endocrine functions increases, attention is focused on the effects of synthetic progestins as environmental endocrine disrupters. In the present study, effects of exposure to a synthetic progestin (norethindrone, 168 ± 7.5 ng/L) and endogenous progestogen (progesterone, 34 ± 4.1 ng/L) on steroidogenesis in adult female fathead minnows were examined. In vivo exposure to either compound lowered expression (nonsignificant) of luteinizing hormone (LHβ) levels in the brain along with significantly down-regulating the beta isoform of membrane progesterone receptor (mPRβ) in ovary tissue. The correspondence between lowered LHβ levels in the brain and mPRβ in the ovary is suggestive of a possible functional association as positive correlations between LHβ and mPR levels have been demonstrated in other fish species. In vitro exposure of ovary tissue to progesterone resulted in significantly elevated progestogen (pregnenolone, 17α-hydroxyprogesterone, and 17α,20β-dihydroxypregnenone) and androgen (testosterone) production. Whereas in vitro exposure to norethindrone did not significantly impact steroid hormone production but showed decreased testosterone production relative to solvent control (however this was not significant). Overall, this study showed that exposure to a natural progestogen (progesterone) and synthetic progestin (norethindrone), was capable of modulating LHβ (in brain) and mPRβ expression (in ovary).

In silico predicted structural and functional robustness of piscine steroidogenesis

Assessments of metabolic robustness or susceptibility are inherently dependent on quantitative descriptions of network structure and associated function. In this paper a stoichiometric model of piscine steroidogenesis was constructed and constrained with productions of selected steroid hormones. Structural and flux metrics of this in silico model were quantified by calculating extreme pathways and optimal flux distributions (using linear programming). Extreme pathway analysis showed progestin and corticosteroid synthesis reactions to be highly participant in extreme pathways. Furthermore, reaction participation in extreme pathways also fitted a power law distribution (degree exponent γ=2.3), which suggested that progestin and corticosteroid reactions act as 'hubs' capable of generating other functionally relevant pathways required to maintain steady-state functionality of the network. Analysis of cofactor usage (O2 and NADPH) showed progestin synthesis reactions to exhibit high robustness, whereas estrogen productions showed highest energetic demands with low associated robustness to maintain such demands. Linear programming calculated optimal flux distributions showed high heterogeneity of flux values with a near-random power law distribution (degree exponent γ≥2.7). Subsequently, network robustness was tested by assessing maintenance of metabolite flux-sum subject to targeted deletions of rank-ordered (low to high metric) extreme pathway participant and optimal flux reactions. Network robustness was susceptible to deletions of extreme pathway participant reactions, whereas minimal impact of high flux reaction deletion was observed. This analysis shows that the steroid network is susceptible to perturbation of structurally relevant (extreme pathway) reactions rather than those carrying high flux.

Mathematical modeling of the hypothalamic-pituitary-adrenal gland (HPA) axis, including hippocampal mechanisms

This paper presents a mathematical model of the HPA axis. The HPA axis consists of the hypothalamus, the pituitary and the adrenal glands in which the three hormones CRH, ACTH and cortisol interact through receptor dynamics. Furthermore, it has been suggested that receptors in the hippocampus have an influence on the axis. A model is presented with three coupled, non-linear differential equations, with the hormones CRH, ACTH and cortisol as variables. The model includes the known features of the HPA axis, and includes the effects from the hippocampus through its impact on CRH in the hypothalamus. The model is investigated both analytically and numerically for oscillating solutions, related to the ultradian rhythm seen in data, and for multiple fixed points related to hypercortisolemic and hypocortisolemic depression. The existence of an attracting trapping region guarantees that solution curves stay non-negative and bounded, which can be interpreted as a mathematical formulation of homeostasis. No oscillating solutions are present when using physiologically reasonable parameter values. This indicates that the ultradian rhythm originate from different mechanisms. Using physiologically reasonable parameters, the system has a unique fixed point, and the system is globally stable. Therefore, solutions converge to the fixed point for all initial conditions. This is in agreement with cortisol levels returning to normal, after periods of mild stress, in healthy individuals. Perturbing parameters lead to a bifurcation, where two additional fixed points emerge. Thus, the system changes from having a unique stable fixed point into having three fixed points. Of the three fixed points, two are stable and one is unstable. Further investigations show that solutions converge to one of the two stable fixed points depending on the initial conditions. This could explain why healthy people becoming depressed usually fall into one of two groups: a hypercortisolemic depressive group or a hypocortisolemic depressive group.

A computational model to predict rat ovarian steroid secretion from in vitro experiments with endocrine disruptors

A finely tuned balance between estrogens and androgens controls reproductive functions, and the last step of steroidogenesis plays a key role in maintaining that balance. Environmental toxicants are a serious health concern, and numerous studies have been devoted to studying the effects of endocrine disrupting chemicals (EDCs). The effects of EDCs on steroidogenic enzymes may influence steroid secretion and thus lead to reproductive toxicity. To predict hormonal balance disruption on the basis of data on aromatase activity and mRNA level modulation obtained in vitro on granulosa cells, we developed a mathematical model for the last gonadal steps of the sex steroid synthesis pathway. The model can simulate the ovarian synthesis and secretion of estrone, estradiol, androstenedione, and testosterone, and their response to endocrine disruption. The model is able to predict ovarian sex steroid concentrations under normal estrous cycle in female rat, and ovarian estradiol concentrations in adult female rats exposed to atrazine, bisphenol A, metabolites of methoxychlor or vinclozolin, and letrozole.

Constraints-based stoichiometric analysis of hypoxic stress on steroidogenesis in fathead minnows, Pimephales promelas

In this study, an in silico genome-scale metabolic model of steroidogenesis was used to investigate the effects of hypoxic stress on steroid hormone productions in fish. Adult female fathead minnows (Pimephales promelas) were exposed to hypoxia for 7 days with fish sub-sampled on days 1, 3 and 7 of exposure. At each time point, selected steroid enzyme gene expressions and steroid hormone productions were quantified in ovaries. Fold changes in steroid enzyme gene expressions were used to qualitatively scale transcript enzyme reaction constraints (akin to the range of an enzyme’s catalytic activity) in the in silico model. Subsequently, in silico predicted steroid hormone productions were qualitatively compared with experimental results. Key findings were as follows. (1) In silico gene deletion analysis identified highly conserved ‘essential’ genes required for steroid hormone productions. These agreed well (75%) with literature-published genes downregulated in vertebrates (fish and mammal) exposed to hypoxia. (2) Quantification of steroid hormones produced ex vivo from ovaries showed a significant reduction for 17β-estradiol and 17α,20β-dihydroxypregnenone production after 24 h (day 1) of exposure. This lowered 17β-estradiol production was concomitant with downregulation of cyp19a1a gene expression in ovaries. In silico predictions showed agreement with experimentation by predicting effects on estrogen (17β-estradiol and estrone) production. (3) Stochastic sampling of in silico reactions indicated that cholesterol uptake and catalysis to pregnenolone along with estrogen methyltransferase and glucuronidation reactions were also impacted by hypoxia. Taken together, this in silico analysis introduces a powerful model for pathway analysis that can lend insights on the effects of various stressor scenarios on metabolic functions.

Variation in 3β-hydroxysteroid dehydrogenase activity and in pregnenolone supply rate can paradoxically alter androstenedione synthesis

The 3β-hydroxysteroid dehydrogenase/Δ(5)-Δ(4) isomerase (3β-HSD) and 17α-hydroxylase/17,20-lyase cytochrome P450 (P450c17) enzymes are important in determining the balance of the synthesis of different steroids such as progesterone (P4), glucocorticoids, androgens, and estrogens. How this is achieved is not a simple matter because each of the two enzymes utilizes more than one substrate and some substrates are shared in common between the two enzymes. The two synthetic pathways, Δ(4) and Δ(5), are interlinked such that it is difficult to predict how the synthesis of each steroid changes when any of the enzyme activities is varied. In addition, the P450c17 enzyme exhibits different substrate specificities among species, particularly with respect to the 17,20-lyase activity. The mathematical model developed in this study simulates the network of reactions catalyzed by 3β-HSD and P450c17 that characterizes steroid synthesis in human, non-human primate, ovine, and bovine species. In these species, P450c17 has negligible 17,20-lyase activity with the Δ(4)-steroid 17α-hydroxy-progesterone (17OH-P4); therefore androstenedione (A4) is synthesized efficiently only from dehydroepiandrosterone (DHEA) through the Δ(5) pathway. The model helps to understand the interplay between fluxes through the Δ(4) and Δ(5) pathways in this network, and how this determines the response of steroid synthesis to the variation in 3β-HSD activity or in the supply of the precursor substrate, pregnenolone (P5). The model simulations show that A4 synthesis can change paradoxically when 3β-HSD activity is varied. A decrease in 3β-HSD activity to a certain point can increase A4 synthesis by favouring metabolism through the Δ(5) pathway, though further decrease in 3β-HSD activity beyond that point eventually limits A4 synthesis. The model also showed that due to the competitive inhibition of the enzymes' activities by substrates and products, increasing the rate of P5 supply above a certain point can suppress the synthesis of A4, DHEA, and 17OH-P4, and consequently drive more P5 towards P4 synthesis.

A CONSTRAINT-BASED STOICHIOMETRIC MODEL OF THE STEROIDOGENIC NETWORK OF ZEBRAFISH (DANIO RERIO)

The metabolic process of steroidogenesis exhibits a complex biochemical network topology as the activity of various steroidogenic enzymes control cholesterol metabolism to steroid hormone derivatives. In this paper, a stoichiometric reconstruction of a sub-set of 65 reactions from the zebrafish (Danio rerio) steroidogenic network is presented and simulated using uniform reaction constraints. The reconstruction defined a set of 65 enzyme catalyzed reactions and 37 exchange or transport reactions for steroid metabolites. The reconstructed reactions were inclusive of cholesterol and androgen/estrogen metabolism. Biased (statement of network objective function) and un-biased (no statement of objective function) analyses were applied to identify network properties dependent on reaction stoichiometry. Random sampling of flux distributions through the network identified highly-correlated reaction sets that corresponded to the catalysis of steroid metabolites of physiological relevance. Subsequently, optimal flux distributions through network pathways were determined for the production of the three steroidogenic metabolites of: 11-deoxycorticosterone, testosterone and 17β-estradiol. Furthermore, flux variability analyses revealed and confirmed optimal network fluxes through physiologically feasible pathways. The stoichiometric dependence of reactions was also confirmed by conducting deletions of reactions utilized for the optimal production of 17β-estradiol. This paper demonstrates the potential application of constraint-based reconstruction and simulation techniques in enabling the construction of deterministic and predictive physiological models. This acknowledgement is poignant considering the susceptibility of the steroidogenic network to environmental and anthropogenic stressors.

Mechanistic computational model of steroidogenesis in H295R cells: role of oxysterols and cell proliferation to improve predictability of biochemical response to endocrine active chemical--metyrapone

The human adrenocortical carcinoma cell line H295R is being used as an in vitro steroidogenesis screening assay to assess the impact of endocrine active chemicals (EACs) capable of altering steroid biosynthesis. To enhance the interpretation and quantitative application of measurement data in risk assessments, we are developing a mechanistic computational model of adrenal steroidogenesis in H295R cells to predict the synthesis of steroids from cholesterol (CHOL) and their biochemical response to EACs. We previously developed a deterministic model that describes the biosynthetic pathways for the conversion of CHOL to steroids and the kinetics for enzyme inhibition by the EAC, metyrapone (MET). In this study, we extended our dynamic model by (1) including a cell proliferation model supported by additional experiments and (2) adding a pathway for the biosynthesis of oxysterols (OXY), which are endogenous products of CHOL not linked to steroidogenesis. The cell proliferation model predictions closely matched the time-course measurements of the number of viable H295R cells. The extended steroidogenesis model estimates closely correspond to the measured time-course concentrations of CHOL and 14 adrenal steroids both in the cells and in the medium and the calculated time-course concentrations of OXY from control and MET-exposed cells. Our study demonstrates the improvement of the extended, more biologically realistic model to predict CHOL and steroid concentrations in H295R cells and medium and their dynamic biochemical response to the EAC, MET. This mechanistic modeling capability could help define mechanisms of action for poorly characterized chemicals for predictive risk assessments.

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.

Fathead minnow steroidogenesis: in silico analyses reveals tradeoffs between nominal target efficacy and robustness to cross-talk

Background

Interpreting proteomic and genomic data is a major challenge in predictive ecotoxicology that can be addressed by a systems biology approach. Mathematical modeling provides an organizational platform to consolidate protein dynamics with possible genomic regulation. Here, a model of ovarian steroidogenesis in the fathead minnow, Pimephales promelas, (FHM) is developed to evaluate possible transcriptional regulation of steroid production observed in microarray studies.

Results

The model was developed from literature sources, integrating key signaling components (G-protein and PKA activation) with their ensuing effect on steroid production. The model properly predicted trajectory behavior of estradiol and testosterone when fish were exposed to fadrozole, a specific aromatase inhibitor, but failed to predict the steroid hormone behavior occurring one week post-exposure as well as the increase in steroid levels when the stressor was removed. In vivo microarray data implicated three modes of regulation which may account for over-production of steroids during a depuration phase (when the stressor is removed): P450 enzyme up-regulation, inhibin down-regulation, and luteinizing hormone receptor up-regulation. Simulation studies and sensitivity analysis were used to evaluate each case as possible source of compensation to endocrine stress.

Conclusions

Simulation studies of the testosterone and estradiol response to regulation observed in microarray data supported the hypothesis that the FHM steroidogenesis network compensated for endocrine stress by modulating the sensitivity of the ovarian network to global cues coming from the hypothalamus and pituitary. Model predictions of luteinizing hormone receptor regulation were consistent with depuration and in vitro data. These results challenge the traditional approach to network elucidation in systems biology. Generally, the most sensitive interactions in a network are targeted for further elucidation but microarray evidence shows that homeostatic regulation of the steroidogenic network is likely maintained by a mildly sensitive interaction. We hypothesize that effective network elucidation must consider both the sensitivity of the target as well as the target's robustness to biological noise (in this case, to cross-talk) when identifying possible points of regulation.

Computational model of steroidogenesis in human H295R cells to predict biochemical response to endocrine-active chemicals: model development for metyrapone

BACKGROUND

An in vitro steroidogenesis assay using the human adrenocortical carcinoma cell line H295R is being evaluated as a possible screening assay to detect and assess the impact of endocrine-active chemicals (EACs) capable of altering steroid biosynthesis. Data interpretation and their quantitative use in human and ecological risk assessments can be enhanced with mechanistic computational models to help define mechanisms of action and improve understanding of intracellular concentration-response behavior.

OBJECTIVES

The goal of this study was to develop a mechanistic computational model of the metabolic network of adrenal steroidogenesis to estimate the synthesis and secretion of adrenal steroids in human H295R cells and their biochemical response to steroidogenesis-disrupting EAC.

METHODS

We developed a deterministic model that describes the biosynthetic pathways for the conversion of cholesterol to adrenal steroids and the kinetics for enzyme inhibition by metryrapone (MET), a model EAC. Using a nonlinear parameter estimation method, the model was fitted to the measurements from an in vitro steroidogenesis assay using H295R cells.

RESULTS

Model-predicted steroid concentrations in cells and culture medium corresponded well to the time-course measurements from control and MET-exposed cells. A sensitivity analysis indicated the parameter uncertainties and identified transport and metabolic processes that most influenced the concentrations of primary adrenal steroids, aldosterone and cortisol.

CONCLUSIONS

Our study demonstrates the feasibility of using a computational model of steroidogenesis to estimate steroid concentrations in vitro. This capability could be useful to help define mechanisms of action for poorly characterized chemicals and mixtures in support of predictive hazard and risk assessments with EACs.

Relationship of plasma sex steroid concentrations in female fathead minnows to reproductive success and population status

Concentration and/or production of sex steroids such as 17beta-estradiol (E2) and testosterone (T) in fish have commonly been measured in field studies concerned with endocrine-active chemicals. There is a reasonable mechanistic basis for using E2 or T as biomarkers, as chemicals can alter steroid production through both direct and indirect effects on the hypothalamic-pituitary-gonadal (HPG) axis. There is uncertainty, however, as to what changes in steroid status may mean relative to apical endpoints, such as reproduction, that directly affect population status. In this study, we analyzed data from fathead minnow (Pimephales promelas) reproduction studies in which decreases in fecundity were associated with depressed steroid production as a result of chemical exposure. Although the chemicals acted on the HPG axis through different mechanisms, reproductive effects appeared to be expressed through a common pathway, depression of vitellogenin production in females. Plasma concentrations of E2 or T in the females were significantly, positively correlated with fecundity. Linear regression models describing the relationship between E2 or T concentrations and relative fecundity were linked to a population model to predict population trajectories of fathead minnows exposed to chemicals that inhibit steroid production. For example, a population existing at carrying capacity and exposed to a chemical stressor(s) that causes a 50% decrease in E2 production was predicted to exhibit a 92% decrease in population size over a 5-year period. Results of our analysis illustrate a conceptual framework whereby a commonly measured biomarker, sex steroid status, could be linked to individual- and population-level effects in fish.