Systems Biology in Small Fish Models

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

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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
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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_Sys
tems_Biology/Why_Systems_Matter
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The Systems Perspective

• Non-equilibrium thermodynamics (1930s-40s)
• dealt with integration quantitatively
• aimed to discover general principles gtgt
  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.
221249-1252 Wolkenhauer. 2001 Briefings in
Bioinformatics. 2258-270
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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. 119-27
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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.

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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.
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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)

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Compartment
Chemical Probes
Fipronil (-) Muscimol () Apomorphine
() Haloperidol (-) Trilostane (-) Ketoconazole
(-) Fadrozole (-) Prochloraz (-,-) Vinclozolin
(-) Flutamide (-) ß-trebolone () Ethynyl
estradiol ()
GABA
Dopamine
Brain
?
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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
D2 R
Activin R
GPa
FSHb
LHb
Circulating LDL, HDL
Circulating LH, FSH
Blood
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LDL R HDL R
FSH R
LH R
Cholesterol
Outer mitochondrial membrane
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StAR
Inner mitochondrial membrane
P450scc
Gonad (Generalized, gonadal, steroidogenic cell)
Activin
pregnenolone
3bHSD
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Inhibin
progesterone
17a-hydroxyprogesterone
P450c17
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20ßHSD
androstenedione
17ßHSD
17a,20ß-P (MIS)
testosterone
P450arom
P45011ß.
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11ßHSD
11-ketotestosterone
estradiol
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Circulating Sex Steroids / Steroid Hormone
Binding Globlulin
Blood
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Androgen / Estrogen Responsive Tissues (e.g.
liver, fatpad, gonads)
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AR
ER
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Figure 1. Conceptual Overview of Research
Increasing Ecological Relevance
Increasing Diagnostic (Screening) Utility
Molecular mRNA, protein, Enzyme assays
Cellular Metabolite profiles
Organ Functional and Structural change (Pathology)
Individual Altered reproduction or development
Population Decreased numbers of animals
Levels of Biological Organization
Phase 2. Zebrafish genomics proteomics,
metabalomics
Well characterized genome, low eco / regulatory
relevance
Population modeling
Systems modeling
Computational modeling
Phase 1. Fathead minnow 21 d reproduction test
Phase 3. Fathead minnow molecular markers
metabolomics
Poorly characterized genome high eco / regulatory
relevance
?s Depict the flow of information
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A Case Study with Fadrozole
Aromatase
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Ovary
Brain
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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
17b-HSD
20b-HSD
17b-HSD
CYP21
CYP21
CYP19
Testosterone
17b-estradiol
11-deoxycorticosterone
17a20ß-dihydroxy-4-pregnen-3-one
CYP11B1
11-deoxycortisol
CYP11B1
corticosterone
11ß-OH-Testosterone
CYP11B2
CYP11B2
11ßHSD
aldosterone
cortisol
11-Ketotestosterone
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CYP19
Androstenedione
estrone
17b-HSD
17b-HSD
CYP19
Testosterone
17b-estradiol
25oC, 12 h
RIA
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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
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Figure Key
state transition
activated protein
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Fadrozole
Inner mitochondrial membrane
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exposure
post-exposure
post-exposure
exposure
Evidence for compensatory/feedback response to
fadrozole.
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Aromatase A (CYP19A) gene expression
exposure
post-exposure
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Pituitary
LHß
LHR
FSHß
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
17a20ß-dihydroxy-4-pregnen-3-one
CYP11B1
11ß-OH-Testosterone
11ßHSD
11-Ketotestosterone
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P450scc (CYP11A) gene expression
exposure
post-exposure
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FSHR gene expression
exposure
post-exposure
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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?

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A Case Study with Fadrozole

• Supervised low content analyses
• Unsupervised high content analyses

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High-content analyses
Fadrozole
Gonads Brain Liver
High desnity oligonucleotide microarrays
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Enriched Gene Ontology Categories

• Brain
• Cholesterol biosynthesis
• Cholesterol transport
• Bile acid synthesis
• Cytoskeletal components
• Oxidative phosphorylation
• Coagulation/wound response
• Immune response
• Ion transport
• Cell adhesion
• Reproduction
• Nucleotide biosynthesis

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
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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)
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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.
22446-450 Zhao et al. 2006. BMC Bioinformatics
7386 Csete Doyle 2002. Science. 2951664-1669
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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.

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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.