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

ABSTRACT: 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.

SUBMITTER: Shoemaker JE

PROVIDER: S-EPMC2905341 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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

Shoemaker Jason E JE Gayen Kalyan K Garcia-Reyero Natàlia N Perkins Edward J EJ Villeneuve Daniel L DL Liu Li L Doyle Francis J FJ

BMC systems biology 20100628

<h4>Background</h4>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.<h4>Result ...[more]

PMID: 20579396

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Project description:Omics approaches are broadly used to explore endocrine and toxicity-related pathways and functions. Nevertheless, there is still a significant gap in knowledge in terms of understanding the endocrine system and its numerous connections and intricate feedback loops, especially in non-model organisms. The fathead minnow (Pimephales promelas) is a widely used small fish model for aquatic toxicology and regulatory testing, particularly in North America. A draft genome has been published, but the amount of available genomic or transcriptomic information is still far behind that of other more broadly studied species, such as the zebrafish. Here, we used a proteogenomics approach to survey the tissue-specific proteome and transcriptome profiles in adult male fathead minnow. To do so, we generated a draft transcriptome using short and long sequencing reads from liver, testis, brain, heart, gill, head kidney, trunk kidney, and gastrointestinal tract. We identified 30,378 different putative transcripts overall, with the assembled contigs ranging in size from 264 to over 9,720 nts. Over 17,000 transcripts were >1,000 nts, suggesting a robust transcriptome that can be used to interpret RNA sequencing data in the future. We also performed RNA sequencing and proteomics analysis on four tissues, including the telencephalon, hypothalamus, liver, and gastrointestinal tract of male fish. Transcripts ranged from 0 to 600,000 copies per gene and a large portion were expressed in a tissue-specific manner. Specifically, the telencephalon and hypothalamus shared the most expressed genes, while the gastrointestinal tract and the liver were quite distinct. Using protein profiling techniques, we identified a total of 4,045 proteins in the four tissues investigated, and their tissue-specific expression pattern correlated with the transcripts at the pathway level. Similarly to the findings with the transcriptomic data, the hypothalamus and telencephalon had the highest degree of similarity in the proteins detected. The main purpose of this analysis was to generate tissue-specific omics data in order to support future aquatic ecotoxicogenomic and endocrine-related studies as well as to improve our understanding of the fathead minnow as an ecological model.

Dataset Information

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

Publications

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

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