Slide 1

Report
A Systems-Based Approach
for the Characterization of
Toxicity Pathways Associated
with the HPG Axis
A Genomics Timeline
© 2003 Nature Publishing Group
DESIGN BY DARRYL LEJA
PEAS COURTESY J. BLAMIRE, CITY UNIV. NEW YORK; WATSON & CRICK
COURTESY A. BARRINGTON BROWN/SPL; SCIENCE COVERS COURTESY AAAS
(Toxico) Genomics: An Emerging Science*
200
150
2000
100
1000
50
0
0
95 96 97 98 99 00 01 02 03 04 05
Year
Current Contents, 10/05
Toxi cogenom i cs Publ i cat i ons ( )
G enomi cs Publ i cat i ons( )
3000
Biological Responses and Genomics: An Overview
WHAT RESPONSES
ARE POSSIBLE
GENOMICS
INITIATION OF
RESPONSE
TRANSCRIPTOMICS
WHAT DRIVES
THE RESPONSE
Adapted from Viant (2005)
PHENOTYPE
PROTEOMICS
THE METABOLIC
RESPONSE
METABOLOMICS
Transcriptomics: Example of a
Microarray Experiment
Study Goal: Examine fadrozole MOA using a 2,000
gene fathead minnow oligonucleotide array
• Multiple tissues examined
• Multiple differentially-regulated
genes
Spot color
Regulation
Up-regulated genes
Down-regulated genes
No change
Data from EPA/ EcoArray© CRADA
Proteomics: Example of a Gel
Separation/MALDI-TOF MS Experiment
Intens. [a.u.]
Peptide Mass Fingerprinting
x10 4
1091.620
1.5
1799.879
1347.669
1.0
Representative protein expression profile in
testes of control zebrafish
2143.156
1615.722
890.612
1214.658
0.5
1504.667
1978.039
2460.281
2801.340
0.0
1000
1500
2000
Data from EPA-Cincinnati
2500
3000
m /z
Metabolomics: Example of an NMR
Experiment
Fathead Minnow (male)
Sprague-Dawley Rat (male)
Data from EPA-Athens
Toxicogenomics: Benefits
• Unprecedented ability to generate a global
“picture” of the health status of organisms
• Offers unique potential to define MOA/toxicity
pathways from two perspectives
– Identification of key molecular targets/control
nodes
– Linkage of responses across biological levels
of organization
• Both perspectives critical to successful
ecological risk assessments
Toxicogenomics: Challenges
• Amount of data to consider is daunting, well exceeding
historical ecotoxicology bioinformatic capabilities
– Example: ~30,000 genes/100,000
proteins/20,000(?) metabolites (humans)
• Identity of many (sometimes majority) of altered
genes/proteins/metabolites uncertain in key species
for ecotoxicology research
• Even well-defined treatments can cause many
changes, complicating interpretation of biological
significance
– Example: zebrafish exposed to fadrozole - up to
1000 unique up - or down - regulated genes in brain
and gonad (21K microarray)
Conceptual Systems Models
• Can help focus data analysis on relevant
genes/proteins/metabolites
• Provide an a priori framework for formal
hypothesis testing of observed changes
• Allow “discovery” of unanticipated system
components/control nodes key to biological
responses
• Iterative testing/model modification establishes
basis for predictive computational model (s)
1
2
3
4
5
6
7
Brain
8
9
10
11
12
13
14
15
16
18
19
20
Figure Key
state transition
a
Catalysis (including
Liver
activation)
transcriptional activation
b
translational activation
transcription inhibition
c
dissociation
association
d
Genes
mRNA
e
protein
activated
f
protein
g
receptor
Simple
h
molecule
Phenotype
i
j
Pituitary
k
Blood
l
m
n
o
p
Ovary
q
r
Graphical
Systems
Model for
Small Fish
HPG Axis
Characteristics of Conceptual HPG System
• Constructed using Cell Designer® 3.1 coded in
SBML
• Captures 7 functional modules in 6 tissue
compartments with multiple subcompartments
(cell types) for brain, pituitary, ovary and testis
• Assembled from more than 60 primary literature
and review papers on vertebrate HPG axis
• Depicts interactions of over 105 proteins, 40
simple molecules, regulation of 25 genes, and
over 300 reactions relative to our current
understanding of the system
Linkage of Exposure and Effects Using
Genomics, Proteomics, and Metabolomics
in Small Fish Models
• USEPA – Cincinnati, OH
– D. Bencic, I. Knoebl, D. Lattier, J.
Lazorchak, G. Toth, R. Wang
• USEPA – Duluth, MN, and Grosse Isle, MI
– G. Ankley, E. Durhan, K. Jensen, M. Kahl,
L. Makynen, D. Martinovic, D. Miller, D.
Villeneuve
• USEPA – Athens, GA
– T. Collette, D. Ekman, T. Whiteside
• 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.)
• USACOE - Vicksburg, MS
– E. Perkins
• DOE Partners
– Joint Genome Institute, (Walnut
Creek, CA)
– Sandia, (Albuquerque, NM)
– PNNL (Richland, WA)
Compartment
GABA
Dopamine
Brain
?
?
?
PACAP
Pituitary
GnRH
Neuronal
System
GnRH
NPY
D2 R
GABAB
R
Y2 R
GnRH
R
2
Muscimol (+)
3
Apomorphine (+)
4
Haloperidol (-)
Y1 R
Gonadotroph
Activin R
Fipronil (-)
Y2 R
GABAA
R
PAC1 R
1
D1 R
Follistatin
Activin
Chemical “Probes”
D2 R
GPa
FSHb
Blood
Circulating LDL, HDL
LHb
Circulating LH, FSH
LDL R
LH R
5
Trilostane (-)
6
Ketoconazole (-)
FSH R
HDL R
Cholesterol
Outer mitochondrial
membrane
StAR
Inner mitochondrial
membrane
Gonad
Activin
(Generalized, gonadal,
steroidogenic cell)
Inhibin
P450scc
pregnenolone
3bHSD
17α-hydroxyprogesterone
Fadrozole (-)
8
Prochloraz (-,-)
progesterone
P450c17
20βHSD
7
androstenedione
17βHSD
17α,20β-P (MIS)
testosterone
P450arom
9
P45011β.
Vinclozolin (-)
11βHSD
11-ketotestosterone
Blood
Androgen / Estrogen
Responsive Tissues
(e.g. liver, fatpad, gonads)
Circulating Sex Steroids / Steroid
Hormone Binding Globlulin
ER
AR
10
Flutamide (-)
11
β-trebolone (+)
12
Ethynyl estradiol (+)
estradiol
Effects of Aromatase Inhibition on
Reproduction in the Fathead Minnow
150
N
10
N
a
Aromatase Activity
(fmol/mg-1 hr-1)
2
10
6
Male
Female
Control
50
*
*
*
4
Fadrozole
75
c
2
c
CN
0
-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
2 4
6
8 10 12 14 16 18 20
0
Exposure (d)
0
6
E2 (ng/ml)
50
Fadrozole (µg / L)
8
4
*
2
*
0
Vtg (mg/ml)
Cumulative Number of Eggs
(Thousands)
Fadrozole (ug/L)
8
b
20
10
*
*
0
Control
2
10
Fadrozole (µg/l)
*
50
Fadrozole Genomic Analyses
• Fathead minnows exposed to varying concentrations of
fadrozole for 24h or 7d
• Liver, brain and gonad collected and gene expression
assessed by microarray or PCR
• Depending upon statistical criteria, 10s to 100s of genes
changed in each tissue of females exposed for 7 d
• Interpretation questions
– Meaningful to biological function?
– Significant to HPG axis?
– Direct versus compensatory response(s)?
Gene Expression in Fadrozole-Treated
Fathead Minnows:
Hypothesized vs. Observed Changes1
Hypothesized
Observed (Fold)2

-
- Vtg precursor
-
 (18-70)
-Vtg 3 precursor
-
 (7-98)
Zrp

NC3
ERa

 (2.0-2.8)
EST

NP
HSST

NP
CYP3A

 (1.6)
Gene
Vtg
1
Livers from females exposed for 7 d
2
NC, no change; NP, no probe for gene on microarray
Changes in HPG-Relevant Genes in
Fathead Minnows Exposed to Fadrozole1
Gene
Observed (Fold)
Aromatase B
 (9.6)
Cyclin B
 (5.8-7.2)
Cytochrome B
 (13-170)
GAD 65
 (1.2-2.6)
GAD 67
 (1.8-2.6)
HMG-CoA Reductase
 (2.8-5.4)
IGF Binding Protein
 (220)
LDLR Associated Protein
 (1-16)
Pit-1
 (9.4)
PRMC
 (2.1-5.3)
1
Ovaries from females exposed for 7-d
Fadrozole
Paracrine
Inner mitochondrial membrane
transport
Granulosa cell
Theca cell
Linkages Across Biological Levels of
Organization:
Toxicity "Pathways" for HPG-Active
Chemicals
Molecular
• Gene/Protein
Expression
• Metabolite
Profiles
Cellular
Alterations in
production of
signaling molecules
Organ
• Functional changes
• Structural changes
(pathology)
Individual
Population
Altered
reproduction or
development
Decreased
numbers of
animals
Increasing Ecological Relevance
Increasing Diagnostic (Screening) Utility
Linkage of Molecular Responses to
Population Effects
• Molecular responses (biomarkers) need to reflect
toxicity pathways of concern
• Molecular responses also require biologically
plausible (ideally quantitative) linkages to adverse
outcomes in individuals
• Individual outcomes need to be easily translated
into ecologically-realistic population context
Vitellogenin as a Biomarker:
Linkage to Population-Level Effects
• Decreases in Vtg in females are a consistent (and
mechanistically reasonable) response to decreases in
steroidogenesis caused by chemicals
• Vtg status also has been hypothesized as a direct
indicator of female’s ability to produce eggs
• If the relationship “holds”, population-level responses
based on Vtg should be possible
Lab Test Data
Life Table - Leslie Matrix
Carrying Capacity/Habitat Quality
Density-Dependent
Model for Population
Prediction
Model Application and Results
Fecundity
in Fecundity
Decrease
Measurement of vtg concentrations and
fecundity for female fathead minnows
17β-trenbolone
Projection of density dependent
logistic population trajectories for
the fathead minnow population
based upon change in vtg
17α-trenbolone
prochloraz
prochloraz
fenarimol
fenarimol
fadrozole
fadrozole
DecreaseVtg
in VTG
Chemical
Life table with age specific vital
rates of survival and fecundity for
the fathead minnow population
Carrying capacity for the fathead minnow
population
Fathead Minnow Fecundity vs Vtg
Exposure Concentrations
1
0.005µg/l, 0.05µg/l, 0.5µg/l, 5µg/l, and 50µg/l
0.003µg/l, 0.01µg/l, 0.03µg/l, and 0.1µg/l
0.03mg/l, 0.1mg/l, and 0.3mg/l
0.1mg/l and 1mg/l
2µg/l, 10µg/l, and 50µg/l
Fecundity = -0.042 + 0.95 * Vtg (R2 = 0.88)
0.9
0.8
0.7
Relative Fecundity
17β-trenbolone
17α-trenbolone
Prochloraz
Fenarimol
Fadrozole
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Relative Vitellogenin
0.8
0.9
1
Model Application and Results
Measurement of vtg concentrations and
fecundity for female fathead minnows
Fecundity
17β-trenbolone
Projection of density dependent
logistic population trajectories for
the fathead minnow population
based upon change in vtg
17α-trenbolone
prochloraz
fenarimol
fadrozole
Vtg
Life table with age specific vital
rates of survival and fecundity for
the fathead minnow population
Carrying capacity for the fathead minnow
population
Population projection for populations
at carrying exposed to stressors that
depress vitellogenin production
Average
Population
Size Size
Average
Population
(Proportion
of
Carrying
Capacity)
(Proportion of Carrying Capacity)
Forecast Population Trajectories
1
1
A A
0%
0.8
0.8
0.6
0.6
0.4
0.4
B B
25%
0.2
0.2
E D
D
0 >95%E 75%
0
C C
50%
0
0
5
5
10
10
Time (Years)
Time (Years)
15
15
20
20
It’s tough to make predictions,
especially about the future.
Yogi Berra
New York Philosopher

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