Systems Biology in Small Fish Models

1. Systems Biology in Small Fish Models D.L. Villeneuve The McKim Conference on Predictive Toxicology Sept. 25-27, 2007, Duluth, Minnesota

2. Systems Biology in Small Fish Models • What is systems biology or the “systems perspective”? • The systems perspective in ecotoxicology. • An overview of a systems-oriented small fish research program. • A case study • Conclusions

3. The Systems Perspective What is a system? A subset of the world whose behavior, and whose interaction with the world, we believe can be sensibly described. • The experimenter can draw a boundary around it, heuristically • The experimenter can conduct defined perturbations within the boundaries • The experimenter can, by reasoning generate sensible explanations for the changed behavior. • Learning is assessed by how well the understanding enables prediction of changed system behavior in response to defined perturbations. Kuipers, B. 1994. MIT Press, Cambridge Brent 2004, Nature Biotech. 22: 1211-1214

4. The Systems Perspective Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. www.oz.net/~geoffsi/bm2003-days/bm2003-20.htm • Systems are comprised of parts which interact. • Interaction of these parts gives rise to "emergent properties". • Emergent properties cannot be attributed to any single parts of the system. Irreducible. • To understand systems, and to be able to fully understand a system's emergent properties, systems need be studied as a whole. http://www.systemsbiology.org/Intro_to_ISB_and_Systems_Biology/Why_Systems_Matter

5. The Systems Perspective • Non-equilibrium thermodynamics (1930s-40s) • dealt with integration quantitatively • aimed to discover general principles >> descriptive • established connection to molecular mechanisms • Investigation of biological self-organization (1950s) • examination of how structures, oscillations, and/or waves arise in a steady or homogenous environment • Feedback regulation in metabolism (late 1950s) • Systems theory in biology (1960s) • “search for general biological laws governing behavior and evolution of living matter in a way analogous to the relation of physical laws and non-living matter” • Metabolic control analysis (1970s) • Approaches to characterize properties of networks of interacting chemical reactions • Convergence with “mainstream” molecular biology – high throughput, genome-scale, “data-rich” Westerhoff and Palsson, 2004 Nature Biotech. 22:1249-1252 Wolkenhauer. 2001 Briefings in Bioinformatics. 2:258-270

6. The Systems Perspective • Shift from reductionism to a holistic perspective • Should reduce complexity rather than adding additional layers of complexity • Search for organizing principles over construction of predictive descriptions (models) that exactly describe the evolution of a system in space and time. • Identification of new concepts and hypotheses that provide a conceptual structure with logical coherence to rival chemistry and physics. Mesarovic et al. 2004. Syst. Biol. 1:19-27

7. The Systems Perspective in Ecotoxicology • Shift from reductionism to a holistic perspective • Historically, ecotoxicology and ecological risk assessment was holistic in focus • Apical/integrative endpoints: Survival, growth, reproduction • Reductionist in the sense that testing of the universe of chemicals was the paradigm. • Not feasible to test every chemical, let alone every chemical mixture • Increased emphasis on subtle, chronic impacts (e.g., development, behavior, etc.) with long-term population implications.

8. The Systems Perspective in Ecotoxicology Search for organizing principles that underlie biological response to stressors. Develop a conceptual structure with logical coherence that allows us to predict, with reasonable and quantitative certainty, integrated, ecologically-relevant impacts.

9. Linkage of Exposure and Effects Using Genomics, Proteomics, and Metabolomics in Small Fish Models • USEPA – Cincinnati, OH • D. Bencic, M. Kostich, I. Knoebl, D. Lattier, J. Lazorchak, G. Toth, R. Wang, • USEPA – Duluth, MN, and Grosse Isle, MI • G. Ankley, E Durhan, M Kahl, K Jensen, E Makynen, D. Martinovic, D. Miller, D. Villeneuve, • USEPA – Athens, GA • T. Collette, D. Ekman, J. Kenneke, T. Whitehead, Q. Teng • USEPA-RTP, NC • M. Breen, R. Conolly • USEPA STAR Program • N. Denslow (Univ. of Florida), E. Orlando, (Florida Atlantic University), K. Watanabe (Oregon Health Sciences Univ.), M. Sepulveda (Purdue Univ.) • USACE – Vicksburg, MS • E. Perkins • Other partners • Joint Genome Institute, DOE (Walnut Creek, CA) • Sandia, DOE (Albuquerque, NM) • Pacific Northwest National Laboratory (Richland, WA) • C. Tyler (Univ. Exeter, UK)

11. Compartment Chemical “Probes” Fipronil* (-) Muscimol* (+) Apomorphine (+) Haloperidol (-) Trilostane (-) Ketoconazole (-) Fadrozole (-) Prochloraz (-,-) Vinclozolin (-) Flutamide (-) β-trebolone (+) Ethynyl estradiol (+) GABA Dopamine Brain ? 1 ? GnRH Neuronal System D1 R Y2 R 2 GnRH NPY GABAA R ? D2 R GABAB R PACAP Y2 R Pituitary 3 Follistatin GnRH R Activin PAC1 R Gonadotroph Y1 R 4 Activin R D2 R GPa FSHb LHb Circulating LDL, HDL Blood Circulating LH, FSH 5 LDL R HDL R LH R FSH R Cholesterol Outer mitochondrial membrane 6 StAR Inner mitochondrial membrane P450scc Gonad (Generalized, gonadal, steroidogenic cell) Activin pregnenolone 3bHSD 7 Inhibin 17α-hydroxyprogesterone progesterone P450c17 8 20βHSD androstenedione 17βHSD 17α,20β-P (MIS) testosterone P450arom P45011β. 9 11βHSD 11-ketotestosterone estradiol 10 Circulating Sex Steroids / Steroid Hormone Binding Globlulin Blood 11 Androgen / Estrogen Responsive Tissues (e.g. liver, fatpad, gonads) 12 ER AR

12. Figure 1. Conceptual Overview of Research Increasing Diagnostic (Screening) Utility Increasing Ecological Relevance Levels of Biological Organization Molecular mRNA, protein, Enzyme assays Cellular Metabolite profiles Organ Functional and Structural change (Pathology) Individual Altered reproduction or development Population Decreased numbers of animals Well characterized genome, low eco / regulatory relevance Phase 2. Zebrafish genomics proteomics, metabalomics Computational modeling Systems modeling Population modeling Poorly characterized genome high eco / regulatory relevance Phase 3. Fathead minnow molecular markers metabolomics Phase 1. Fathead minnow 21 d reproduction test →’s Depict the flow of information

13. A Case Study withFadrozole Aromatase

14. Ovary Brain

15. A Case Study with Fadrozole Cholesterol CYP11A CYP17 (hydroxylase) CYP17 (lyase) Pregnenolone 17a-OH-Pregnenolone DHEA 3b-HSD 3b-HSD 3b-HSD CYP17 (hydroxylase) CYP17 (lyase) CYP19 Progesterone 17a-OH-Progesterone Androstenedione estrone 20b-HSD 17b-HSD 17b-HSD CYP21 CYP21 CYP19 11-deoxycorticosterone Testosterone 17b-estradiol 17α20β-dihydroxy-4-pregnen-3-one 11-deoxycortisol CYP11B1 CYP11B1 corticosterone 11β-OH-Testosterone CYP11B2 CYP11B2 11βHSD aldosterone cortisol 11-Ketotestosterone

16. CYP19 Androstenedione estrone 17b-HSD 17b-HSD CYP19 Testosterone 17b-estradiol 25oC, 12 h RIA

18. Brain sGnRH neurons cGnRH neurons dopaminergic neurons sGnRH release cGnRH release dopamine release GnRH Pituitary FSH gonadotrophs LH gonadotrophs Blood supply Blood supply FSH LH Testis Ovaries Leydig cells Sertoli cells Sperm- atocytes Theca cells Granulosa cells Oocytes Sperm mat. oocyte mat. steroids steroids Liver Steroid metab. vitellogenesis

19. genes Gonadotropin expression and secretion mRNA Steroid metabolism protein receptor GnRH release pheno-type transcriptional activation translational activation transcription inhibition dissociation Vitellogenesis and Oocyte maturation Steroidogenesis Cholesterol transport association Figure Key state transition activated protein

20. Figure Key state transition genes mRNA protein receptor pheno-type activated protein transcriptional activation translational activation transcription inhibition dissociation association Fadrozole Inner mitochondrial membrane

21. exposure post-exposure post-exposure exposure Evidence for compensatory/feedback response to fadrozole.

22. Aromatase A (CYP19A) gene expression exposure post-exposure

23. Pituitary LHβ LHR FSHβ Cholesterol StAR FSHR G-protein mediated Signal transduction Inner mitochondrial membrane Cyt b5 Cholesterol CYP11A CYP17 (hydroxylase) CYP17 (lyase) Pregnenolone 17a-OH-Pregnenolone DHEA 3b-HSD 3b-HSD 3b-HSD CYP17 (hydroxylase) CYP17 (lyase) CYP19 Progesterone 17a-OH-Progesterone Androstenedione estrone 17b-HSD 17b-HSD 20b-HSD CYP19 Testosterone 17b-estradiol 17α20β-dihydroxy-4-pregnen-3-one CYP11B1 11β-OH-Testosterone 11βHSD Fig. 1 11-Ketotestosterone

24. P450scc (CYP11A) gene expression exposure post-exposure

25. FSHR gene expression exposure post-exposure

26. Despite evidence for compensation at the molecular and biochemical level, 21 d exposure to fadrozole causes adverse apical effect on reproduction. • Under what conditions would compensation be successful? • Under what conditions does it fail to prevent adverse effect? • Under what conditions does it contribute to or exacerbate the initial impact of the stressor?

27. A Case Study withFadrozole • Supervised – low content analyses • Unsupervised – high content analyses

28. High-content analyses Fadrozole Gonads Brain Liver High desnity oligonucleotide microarrays

30. Enriched Gene Ontology Categories Liver Ribosomal proteins Ribosomal RNAs Translation Oocytes/oogenesis Iron transport/plasma proteins Immune response and inflammation Cytoskeletal components Ovary Ribosomal proteins Ribosomal RNAs Extracellular matrix Connective tissue Coagulation/wound response Cytoskeletal components Oxidative phosphorylation Ion transport Embryonic development Cell adhesion Oogenesis Brain Cholesterol biosynthesis Cholesterol transport Bile acid synthesis Cytoskeletal components Oxidative phosphorylation Coagulation/wound response Immune response Ion transport Cell adhesion Reproduction Nucleotide biosynthesis

31. Reverse engineering gene regulatory architecture Different reverse engineering algorithms. Dynamic Bayesian Network (Yu et al 2004, Zhao et al 2006 ) Boolean (Hashimoto et al 2004) Information theory (Margolin et al 2006)

32. Network Approach • Start with complex system • Determine players and links in network • Calculate network statistics • Use network statistics to make predictions about network function • Robustness and fragility in ecosystems and gene networks is related to network architecture (sensitivity or insensitivity to network perturbation) • In general, more interactions = more plasticity • Biological systems – generally many nodes with relatively few links, and relatively few nodes with many links (critical regulatory nodes) Csete & Doyle 2004 Trends in Biotechnol. 22:446-450 Zhao et al. 2006. BMC Bioinformatics 7:386 Csete & Doyle 2002. Science. 295:1664-1669

33. CONCLUSIONS • Consideration of interactions and relationships is at the heart of the systems perspective. Systems models serve as a critical translator between initiating events (modeled by QSAR) and biological outcome. • Systems models need not be infinitely detailed. Resolution required defined by the questions to be answered. • Toxicity pathways linking exposure to adverse effect include the direct effect of the chemical and a wide variety of indirect secondary, tertiary….and potentially stochastic effects that influence the apical outcome. • Molecular and biochemical responses to stressors are both dose and time-dependent, and best represented in three dimensions. Important consideration for biomarker-based bioassay and high-throughput screening.

34. CONCLUSIONS • Modern “omics” tools facilitate unprecedented scale and detail in the descriptive analysis of biological systems. The greater challenge is in defining relationships and the general principles that govern them. • Overall architecture of the systems or networks can reveal important attributes related to function. • Hypothesis driven and unsupervised analysis of biological systems or networks can reveal critical regulatory nodes that integrate signals and drive component function or phenotype. • The ultimate goal of systems ecotoxicology is discovery of generalized or universal principles that govern biological responses to stressors.