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Mathematical Modeling of RNA Virus Sensing Pathways Reveals Paracrine Signaling as the Primary Factor Regulating Excessive Cytokine Production. Processes (Basel) 2020. [DOI: 10.3390/pr8060719] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RNA viruses, such as influenza and Severe Acute Respiratory Syndrome (SARS), invoke excessive immune responses; however, the kinetics that regulate inflammatory responses within infected cells remain unresolved. Here, we develop a mathematical model of the RNA virus sensing pathways, to determine the intracellular events that primarily regulate interferon, an important protein for the activation and management of inflammation. Within the ordinary differential equation (ODE) model, we incorporate viral replication, cell death, interferon stimulated genes’ antagonistic effects on viral replication, and virus sensor protein (TLR and RIG-I) kinetics. The model is parameterized to influenza infection data using Markov chain Monte Carlo and then validated against infection data from an NS1 knockout strain of influenza, demonstrating that RIG-I antagonism significantly alters cytokine signaling trajectory. Global sensitivity analysis suggests that paracrine signaling is responsible for the majority of cytokine production, suggesting that rapid cytokine production may be best managed by influencing extracellular cytokine levels. As most of the model kinetics are host cell specific and not virus specific, the model presented provides an important step to modeling the intracellular immune dynamics of many RNA viruses, including the viruses responsible for SARS, Middle East Respiratory Syndrome (MERS), and Coronavirus Disease (COVID-19).
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Perkins EJ, Ashauer R, Burgoon L, Conolly R, Landesmann B, Mackay C, Murphy CA, Pollesch N, Wheeler JR, Zupanic A, Scholz S. Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:1850-1865. [PMID: 31127958 PMCID: PMC6771761 DOI: 10.1002/etc.4505] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/26/2019] [Accepted: 05/21/2019] [Indexed: 05/20/2023]
Abstract
An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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Affiliation(s)
- Edward J. Perkins
- US Army Engineer Research and Development CenterVicksburgMississippiUSA
| | - Roman Ashauer
- Environment DepartmentUniversity of York, HeslingtonYorkUK
- ToxicodynamicsYorkUK
| | - Lyle Burgoon
- US Army Engineer Research and Development CenterVicksburgMississippiUSA
| | - Rory Conolly
- Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and DevelopmentUS Environmental Protection Agency, Research Triangle ParkNorth CarolinaUSA
| | | | - Cameron Mackay
- Unilever Safety and Environmental Assurance Centre, SharnbrookBedfordUK
| | - Cheryl A. Murphy
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | - Nathan Pollesch
- Mid‐Continent Ecology Division, National Health and Environmental Effects Laboratory, Office of Research and DevelopmentUS Environmental Protection AgencyDuluthMinnesotaUSA
| | | | - Anze Zupanic
- Department of Environmental ToxicologySwiss Federal Institute for Aquatic Science and TechnologyDübendorfSwitzerland
| | - Stefan Scholz
- Department of Bioanalytical EcotoxicologyHelmholtz Centre for Environmental Research‐UFZLeipzigGermany
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Ayobahan SU, Eilebrecht E, Kotthoff M, Baumann L, Eilebrecht S, Teigeler M, Hollert H, Kalkhof S, Schäfers C. A combined FSTRA-shotgun proteomics approach to identify molecular changes in zebrafish upon chemical exposure. Sci Rep 2019; 9:6599. [PMID: 31036921 PMCID: PMC6488664 DOI: 10.1038/s41598-019-43089-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/15/2019] [Indexed: 11/09/2022] Open
Abstract
The fish short-term reproduction assay (FSTRA) is a common in vivo screening assay for assessing endocrine effects of chemicals on reproduction in fish. However, the current reliance on measures such as egg number, plasma vitellogenin concentration and morphological changes to determine endocrine effects can lead to false labelling of chemicals with non-endocrine modes- of-action. Here, we integrated quantitative liver and gonad shotgun proteomics into the FSTRA in order to investigate the causal link between an endocrine mode-of-action and adverse effects assigned to the endocrine axis. Therefore, we analyzed the molecular effects of fadrozole-induced aromatase inhibition in zebrafish (Danio rerio). We observed a concentration-dependent decrease in fecundity, a reduction in plasma vitellogenin concentrations and a mild oocyte atresia with oocyte membrane folding in females. Consistent with these apical measures, proteomics revealed a significant dysregulation of proteins involved in steroid hormone secretion and estrogen stimulus in the female liver. In the ovary, the deregulation of estrogen synthesis and binding of sperm to zona pellucida were among the most significantly perturbed pathways. A significant deregulation of proteins targeting the transcriptional activity of estrogen receptor (esr1) was observed in male liver and testis. Our results support that organ- and sex-specific quantitative proteomics represent a promising tool for identifying early gene expression changes preceding chemical-induced adverse outcomes. These data can help to establish consistency in chemical classification and labelling.
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Affiliation(s)
- Steve U Ayobahan
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Schmallenberg, Germany. .,Institute of Environmental Research (Biology V), RWTH Aachen, Aachen, Germany.
| | - Elke Eilebrecht
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Schmallenberg, Germany.
| | - Matthias Kotthoff
- Department 2, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany
| | - Lisa Baumann
- Aquatic Ecology & Toxicology, University of Heidelberg, Heidelberg, Germany
| | - Sebastian Eilebrecht
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Schmallenberg, Germany
| | - Matthias Teigeler
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Schmallenberg, Germany
| | - Henner Hollert
- Institute of Environmental Research (Biology V), RWTH Aachen, Aachen, Germany
| | - Stefan Kalkhof
- Institute for Bioanalysis, University of Applied Sciences Coburg, Coburg, Germany
| | - Christoph Schäfers
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Schmallenberg, Germany
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Gregg RW, Sarkar SN, Shoemaker JE. Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback. J Theor Biol 2019; 462:148-157. [DOI: 10.1016/j.jtbi.2018.11.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 10/09/2018] [Accepted: 11/01/2018] [Indexed: 01/12/2023]
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In silico predicted transcriptional regulatory control of steroidogenesis in spawning female fathead minnows (Pimephales promelas). J Theor Biol 2018; 455:179-190. [PMID: 30036528 DOI: 10.1016/j.jtbi.2018.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 07/16/2018] [Accepted: 07/18/2018] [Indexed: 11/21/2022]
Abstract
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.
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Conolly RB, Ankley GT, Cheng W, Mayo ML, Miller DH, Perkins EJ, Villeneuve DL, Watanabe KH. Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:4661-4672. [PMID: 28355063 PMCID: PMC6134852 DOI: 10.1021/acs.est.6b06230] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.
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Affiliation(s)
- Rory B. Conolly
- U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division, Research Triangle Park, NC 27709, USA
- Corresponding Author: Rory Conolly, U.S. EPA ORD/NHEERL/ISTD, MD B105-03, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA, +1 919-541-3350,
| | - Gerald T. Ankley
- U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, MN 55804, USA
| | - WanYun Cheng
- U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division, Research Triangle Park, NC 27709, USA
| | - Michael L. Mayo
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
| | - David H. Miller
- U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Grosse Isle, MI 48138, USA
| | - Edward J. Perkins
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
| | - Daniel L. Villeneuve
- U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, MN 55804, USA
| | - Karen H. Watanabe
- School of Mathematical and Natural Sciences, Arizona State University, West Campus, Glendale, AZ 85306, USA
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Perkins EJ, Antczak P, Burgoon L, Falciani F, Garcia-Reyero N, Gutsell S, Hodges G, Kienzler A, Knapen D, McBride M, Willett C. Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence. Toxicol Sci 2016; 148:14-25. [PMID: 26500288 DOI: 10.1093/toxsci/kfv181] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.
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Affiliation(s)
- Edward J Perkins
- *Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg Mississippi;
| | - Philipp Antczak
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside L69 3BX, UK
| | - Lyle Burgoon
- *Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg Mississippi
| | - Francesco Falciani
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside L69 3BX, UK
| | - Natàlia Garcia-Reyero
- Mississippi State University, Institute for Genomics, Biocomputing and Biotechnology, Starkville, Mississippi
| | - Steve Gutsell
- Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Geoff Hodges
- Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Aude Kienzler
- JRC Institute for Health and Consumer Protection, Ispra, Italy
| | - Dries Knapen
- University of Antwerp, Zebrafishlab, Universiteitsplein 1, 2610 Wilrijk, Belgium
| | - Mary McBride
- Agilent Technologies, Washington, District of Columbia; and
| | - Catherine Willett
- The Humane Society of the United States, Washington, District of Columbia, USA
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8
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Gust KA, Collier ZA, Mayo ML, Stanley JK, Gong P, Chappell MA. Limitations of toxicity characterization in life cycle assessment: Can adverse outcome pathways provide a new foundation? INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2016; 12:580-590. [PMID: 26331849 DOI: 10.1002/ieam.1708] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 05/05/2015] [Accepted: 08/20/2015] [Indexed: 06/05/2023]
Abstract
Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580-590. Published 2015. This article is a US Government work and is in the public domain in the USA.
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Affiliation(s)
- Kurt A Gust
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
| | - Zachary A Collier
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
| | - Michael L Mayo
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
| | - Jacob K Stanley
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
| | - Ping Gong
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
| | - Mark A Chappell
- US Army Engineer Research & Development Center, Vicksburg, Mississippi
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Gillies K, Krone SM, Nagler JJ, Schultz IR. A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis. PLoS Comput Biol 2016; 12:e1004874. [PMID: 27096735 PMCID: PMC4838294 DOI: 10.1371/journal.pcbi.1004874] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 03/17/2016] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
- Kendall Gillies
- Battelle, Pacific Northwest National Laboratory, Marine Sciences Laboratory, Sequim, Washington, United States of America
| | - Stephen M. Krone
- University of Idaho, Department of Mathematics, Moscow, Idaho, United States of America
| | - James J. Nagler
- University of Idaho, Department of Biological Sciences and Center for Reproductive Biology, Moscow, Idaho, United States of America
| | - Irvin R. Schultz
- Battelle, Pacific Northwest National Laboratory, Marine Sciences Laboratory, Sequim, Washington, United States of America
- * E-mail:
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Garcia-Reyero N, Tingaud-Sequeira A, Cao M, Zhu Z, Perkins EJ, Hu W. Endocrinology: advances through omics and related technologies. Gen Comp Endocrinol 2014; 203:262-73. [PMID: 24726988 DOI: 10.1016/j.ygcen.2014.03.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Revised: 03/20/2014] [Accepted: 03/22/2014] [Indexed: 12/27/2022]
Abstract
The rapid development of new omics technologies to measure changes at genetic, transcriptomic, proteomic, and metabolomics levels together with the evolution of methods to analyze and integrate the data at a systems level are revolutionizing the study of biological processes. Here we discuss how new approaches using omics technologies have expanded our knowledge especially in nontraditional models. Our increasing knowledge of these interactions and evolutionary pathway conservation facilitates the use of nontraditional species, both invertebrate and vertebrate, as new model species for biological and endocrinology research. The increasing availability of technology to create organisms overexpressing key genes in endocrine function allows manipulation of complex regulatory networks such as growth hormone (GH) in transgenic fish where disregulation of GH production to produce larger fish has also permitted exploration of the role that GH plays in testis development, suggesting that it does so through interactions with insulin-like growth factors. The availability of omics tools to monitor changes at nearly any level in any organism, manipulate gene expression and behavior, and integrate data across biological levels, provides novel opportunities to explore endocrine function across many species and understand the complex roles that key genes play in different aspects of the endocrine function.
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Affiliation(s)
- Natàlia Garcia-Reyero
- Institute for Genomics Biocomputing and Biotechnology, Mississippi State University, Starkville, MS 39759, USA.
| | - Angèle Tingaud-Sequeira
- Laboratoire MRMG, Maladies Rares: Génétique et Métabolisme, Université de Bordeaux, 33405 Talence Cedex, France
| | - Mengxi Cao
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Zuoyan Zhu
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Edward J Perkins
- US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
| | - Wei Hu
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
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11
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Li Z, Kroll KJ, Jensen KM, Villeneuve DL, Ankley GT, Brian JV, Sepúlveda MS, Orlando EF, Lazorchak JM, Kostich M, Armstrong B, Denslow ND, Watanabe KH. A computational model of the hypothalamic: pituitary: gonadal axis in female fathead minnows (Pimephales promelas) exposed to 17α-ethynylestradiol and 17β-trenbolone. BMC SYSTEMS BIOLOGY 2011; 5:63. [PMID: 21545743 PMCID: PMC3118352 DOI: 10.1186/1752-0509-5-63] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Accepted: 05/05/2011] [Indexed: 11/10/2022]
Abstract
Background Endocrine disrupting chemicals (e.g., estrogens, androgens and their mimics) are known to affect reproduction in fish. 17α-ethynylestradiol is a synthetic estrogen used in birth control pills. 17β-trenbolone is a relatively stable metabolite of trenbolone acetate, a synthetic androgen used as a growth promoter in livestock. Both 17α-ethynylestradiol and 17β-trenbolone have been found in the aquatic environment and affect fish reproduction. In this study, we developed a physiologically-based computational model for female fathead minnows (FHM, Pimephales promelas), a small fish species used in ecotoxicology, to simulate how estrogens (i.e., 17α-ethynylestradiol) or androgens (i.e., 17β-trenbolone) affect reproductive endpoints such as plasma concentrations of steroid hormones (e.g., 17β-estradiol and testosterone) and vitellogenin (a precursor to egg yolk proteins). Results Using Markov Chain Monte Carlo simulations, the model was calibrated with data from unexposed, 17α-ethynylestradiol-exposed, and 17β-trenbolone-exposed FHMs. Four Markov chains were simulated, and the chains for each calibrated model parameter (26 in total) converged within 20,000 iterations. With the converged parameter values, we evaluated the model's predictive ability by simulating a variety of independent experimental data. The model predictions agreed with the experimental data well. Conclusions The physiologically-based computational model represents the hypothalamic-pituitary-gonadal axis in adult female FHM robustly. The model is useful to estimate how estrogens (e.g., 17α-ethynylestradiol) or androgens (e.g., 17β-trenbolone) affect plasma concentrations of 17β-estradiol, testosterone and vitellogenin, which are important determinants of fecundity in fish.
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Affiliation(s)
- Zhenhong Li
- Division of Environmental and Biomolecular Systems, Oregon Health & Science University, Beaverton, USA
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12
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Garcia-Reyero N, Perkins EJ. Systems biology: leading the revolution in ecotoxicology. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2011; 30:265-273. [PMID: 21072840 DOI: 10.1002/etc.401] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
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.
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