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Office of Research and Development
Applying Bioinformatics to Chemical Risk Assessment: Assays, Databases, ModelsRichard JudsonU.S. EPA, National Center for Computational ToxicologyOffice of Research and Development
The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA
3rd Danish Bioinformatics Conference
24 August2017
Office of Research and DevelopmentNational Center for Computational Toxicology
National Center for Computational Toxicology• National Center for Computational Toxicology established in 2005 to
integrate:– High-throughput and high-content technologies– Modeling and data mining– Modern molecular biology– Computational biology and chemistry
• Currently staffed by ~60 employees– PIs, Postdocs, grad students, support staff
• EPA’s Office of R&D
Office of Research and DevelopmentNational Center for Computational Toxicology
Bioinformatics: From Society to Sequences• Society puts T tons of chemical X into the environment• Person P is exposed to Y amount• Can we predict T, Y?• Can we predict the effect on P at the level of molecules, cells, tissues, organs?
• Need to model biology that is multiscale, complex, non-linear• Multiple and often unknown sources of:
–Noise–Variation
• Goal: use data, models, algorithms to make predictions• Predictions are statistical, probabilistic 3
Office of Research and DevelopmentNational Center for Computational Toxicology
How far can we go with modeling biology?
4
Biology and computer science (e.g. bioinformatics) tell us that we are just algorithms embedded in flesh, soon to be replaced by silicon and metal. We will worship Information and our hearts will belong to Data
Paraphrase of Steven Shapin, on the coming cyborg future
We’re all just cyborgs, random clanking assemblages inserted in circuits way beyond our understanding
Jenny Turner, commenting on Steven Shapin
Office of Research and DevelopmentNational Center for Computational Toxicology 5
MultiscaleChemicalMouthGI TractBloodLiverTissuesCellsProteins/DNAMolecular CircuitsCellsTissueOrganOrganism
Office of Research and DevelopmentNational Center for Computational Toxicology
Exposure
mg/kg BW/day
Hazard: Kinetics
+Dynamics
LowPriority
MediumPriority
HighPriority
Risk-based ApproachHazard + Exposure (+ uncertainty)
Kine
tics
Dyn
amic
s
Office of Research and DevelopmentNational Center for Computational Toxicology
Computational Toxicology
• Identify biological pathways of toxicity (AOPs)
• Develop high-throughput in vitro assays
–Test “Human Exposure Universe” chemicals in the assays
• Develop models that link in vitro to in vivo hazard
• Develop exposure models
• Add uncertainty estimatesKi
netic
s
Dyn
amic
s
Office of Research and DevelopmentNational Center for Computational Toxicology
High-Throughput Screening 101 (HTS)
8
8
96-, 384-, 1536 Well Plates
Target Biology (e.g., Estrogen Receptor)
Robots
Pathway
Cell Population
AC50LEC
Emax
Conc (uM)R
espo
nse
Chemical Exposure
Office of Research and DevelopmentNational Center for Computational Toxicology
Typical Risk Assessment Problem:Identifying Endocrine Disruptors
• Chemicals that mimic natural sex hormones can have significant effects years to decades after exposure (e.g. DES)
• There are 10,000s of chemicals to be evaluated
• Can we screen for these in an efficient way?
• Can we understand the uncertainty of our results?
9
Office of Research and DevelopmentNational Center for Computational Toxicology
In Vitro Estrogen Receptor Model
10
• No in vitro assay is perfect• Assay Interference• Noise
• Use multiple assays per pathway• Different technologies• Different points in pathway
• Use model to integrate assays
• Evaluate model against reference chemicals
Judson et al: “Integrated Model of Chemical Perturbations of a Biological PathwayUsing 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor” (EHP 2015)
Office of Research and DevelopmentNational Center for Computational Toxicology
ER Receptor Binding(Agonist)
Dimerization
CofactorRecruitment
DNA Binding
RNA Transcription
Protein Production
ER-inducedProliferation
R3
R1
R5
R7
R8
R6
N1
N2
N3
N4
N5
N6
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A12
A13
A14
A15
A16
A11
Receptor (Direct Molecular Interaction)
Intermediate Process
Assay
ER agonist pathway
Pseudo-receptor pathway
ER antagonist pathway
R2
N7
ER Receptor Binding
(Antagonist)
A17
A18
Dimerization
N8
N9DNA Binding
CofactorRecruitment
N10AntagonistTranscriptionSuppression
R4
R9
A1
ATG TRANSATG CIS
Tox21 BLATox21 LUC
Tox21 BLATox21 LUC ACEA
OT PCA αα,αβ,ββ
OT Chromatin Binding
NVSbovinehumanmouse
Office of Research and DevelopmentNational Center for Computational Toxicology
All In vitro assays have false positives and negatives
Much of this “noise” is reproducible- “assay interference”- Result of interaction of chemical
with complex biology in the assay
Chemical universe is structurally diverse-Solvents-Surfactants-Intentionally cytotoxic compounds-Metals-Inorganics-Pesticides-Drugs
Assays cluster by technology,suggesting technology-specific
non-ER bioactivity
Judson et al: ToxSci (2015)
Che
mic
als
Assays
Office of Research and DevelopmentNational Center for Computational Toxicology
Schematic explanation of non-specific activity
13
Oxidative StressDNA ReactivityProtein ReactivityMitochondrial stress
ER stressCell membrane disruptionSpecific apoptosis…
Specific Non-specific
Office of Research and DevelopmentNational Center for Computational Toxicology
ER Receptor Binding(Agonist)
Dimerization
CofactorRecruitment
DNA Binding
RNA Transcription
Protein Production
ER-inducedProliferation
R3
R1
R5
R7
R8
R6
N1
N2
N3
N4
N5
N6
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A12
A13
A14
A15
A16
A11
Receptor (Direct Molecular Interaction)
Intermediate Process
Assay
ER agonist pathway
Pseudo-receptor pathway
ER antagonist pathway
R2
N7
ER Receptor Binding
(Antagonist)
A17
A18
Dimerization
N8
N9DNA Binding
CofactorRecruitment
N10AntagonistTranscriptionSuppression
R4
R9
A1
ATG TRANSATG CIS
Tox21 BLATox21 LUC
Tox21 BLATox21 LUC ACEA
OT PCA αα,αβ,ββ
OT Chromatin Binding
NVSbovinehumanmouse
Office of Research and DevelopmentNational Center for Computational Toxicology
ER Receptor Binding(Agonist)
Dimerization
CofactorRecruitment
DNA Binding
RNA Transcription
Protein Production
ER-inducedProliferation
R3
R1
R5
R7
R8
R6
N1
N2
N3
N4
N5
N6
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A12
A13
A14
A15
A16
A11
R2
N7
ER Receptor Binding
(Antagonist)
A17
A18
Dimerization
N8
N9DNA Binding
CofactorRecruitment
N10AntagonistTranscriptionSuppression
R4
R9
A1Receptor (Direct Molecular Interaction)
Intermediate Process
Assay
ER agonist pathway
Pseudo-receptor pathway
ER antagonist pathway
Office of Research and DevelopmentNational Center for Computational Toxicology
ER Receptor Binding(Agonist)
Dimerization
CofactorRecruitment
DNA Binding
RNA Transcription
Protein Production
ER-inducedProliferation
R3
R1
R5
R7
R8
R6
N1
N2
N3
N4
N5
N6
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A12
A13
A14
A15
A16
A11
R2
N7
ER Receptor Binding
(Antagonist)
A17
A18
Dimerization
N8
N9DNA Binding
CofactorRecruitment
N10AntagonistTranscriptionSuppression
R4
R9
A1Receptor (Direct Molecular Interaction)
Intermediate Process
Assay
ER agonist pathway
Pseudo-receptor pathway
ER antagonist pathway
Office of Research and DevelopmentNational Center for Computational Toxicology
Example chemicals:Observe quantitative uncertainty
17
True Agonist
Assay Interference Example “R3”
Office of Research and DevelopmentNational Center for Computational Toxicology
Compare predicted exposure and ER hazard
18
Hazard
Exposure
Cell-level Modeling: The ‘Virtual Embryo’
Leung et al. (2016) Reprod Toxicol.Zurlinden/Saili et al. (FY17 product).Hunter et al. (FY18 product).Your name here. 19
Genital Tubercle
Vasculature
Palate
Limb-bud
Heart NVU/BBB
Liver / GI
Neural Tube
Renal
Testis / BTB
Delivered Underway Future
Somite
Hester et al. (2011) PLoS Comp Bio; Dias et al (2014) Science Kleinstreuer et al. (2013) PLoS Comp Bio.Ahir et al. (MS in preparation).Hutson et al. (2017) Chem Res Toxicol.
Tom Knudsen Group, EPA NCCT
Model Genital DifferentiationAgent-based models: CompuCell3DAgent=Cell with internal control network
androgen SHH field FGF10 field no androgen
Genital tubercle (GT) Control Network (mouse)
ABM simulation for sexual dimorphism (mouse GD13.5 – 17.5)
20SOURCE: Leung et al. (2016) Reprod Tox
Common Pathway for Fusion: Hypospadias, Cleft Palette, Spina Bifida
• Driven by urethral endoderm (contact, fusion apoptosis) and androgen-dependent effects on preputial mesenchyme (proliferation, condensation, migration) via FGFR2-IIIb.
21
Apply model to chemical insult: Vinclozolin and Hypospadias
SHH FGF
androgen
vinclozolin
21
Common Pathway for Fusion: Hypospadias, Cleft Palette, Spina Bifida
Office of Research and DevelopmentNational Center for Computational Toxicology
What is Adverse?
Tipping Point: Threshold between adaptation and adversity Use ToxCast High Content Imaging
(HCI) data to identify Tipping Points
967 chemica ls (ToxCas t ) HepG2 ce l l s cu l tu re 10 concen t ra t ions 3 Time po in ts 10 HCI Assays 400 p la tes 100 ,000 we l l s• 2 ,400 ,000 images
Discriminating between compensatory changes and changes that will lead to adverse outcomes
Imran Shah group, EPA NCCT
Office of Research and DevelopmentNational Center for Computational Toxicology
High Content Imaging (HCI)
Study HepG2 ce l l cu l ture 967 chemica ls (ToxCast ) 10 conc
HCI Assays Heal th Stress Cel lu lar per turbat ions
Dynamic phenotypic response of ce l ls to chemica ls
Large-scale data• ~400 p la tes• ~100,000 wel ls• ~2,400,000 images
HCI Conducted by Cyprotex, Inc.
High-Content Imaging (HCI): multiplexed
measurements on cell populations
Office of Research and DevelopmentNational Center for Computational Toxicology
Cell-State Data from Images
Raw Image(Hoechst)
Intensity Analysis
ObjectIdentification
Nuclear intensitydistribution
I m a g e a n a l y s i s a n d c e l l l e v e l f e a t u r e f e a t u r ee x t r a c t i o n c o n d u c t i n g b y C y p r o t e x I n c . ( p r o p r i e t a r y s o f t w a r e )
Office of Research and DevelopmentNational Center for Computational Toxicology
Data for Taxol 0.03uM
Hoechst33342
Phospho-Histone3
MitoTracker Red
Phospho-Tubulin
nuc lear s i ze (NS ) ce l l cyc le a r res t (CCA ) ce l l number (CN )
m i to t i c a r res t (MA )
M i tochondr ia l mass (MM )M i tochondr ia l membranepo ten t ia l (MMP )
m ic ro tubu les (Mt )
Office of Research and DevelopmentNational Center for Computational Toxicology
HCI to Phenotypic States
Derive “phenotypic “states” of HepG2 cel ls across al l chemicals and concentrat ions
Phenotypic states approximate canonical behaviors of cel ls
State t ransi t ions indicate dynamic responses to chemicals
“Sta
tes”
of
Hep
G2
Cel
ls
More adverse
Lessadverse
Office of Research and DevelopmentNational Center for Computational Toxicology
“Normal” State
Fluaz inam 0 .78 uM
0h
System trajectories
Office of Research and DevelopmentNational Center for Computational Toxicology
State Transit ion
Fluaz inam 0 .78 uM
0h
1h
System trajectories
Office of Research and DevelopmentNational Center for Computational Toxicology
System trajectory
Fluaz inam 0 .78 uM
0h
1h
24h24h
System trajectories
Office of Research and DevelopmentNational Center for Computational Toxicology
System trajectories
Fluaz inam 0 .78 uM
0h
1h
24h24h
72h
Tr a j e c t o r y = S e q u e n c e o f s t a t e s
System trajectory
Office of Research and DevelopmentNational Center for Computational Toxicology
Fluazinam “Trajectories”
Increasing Dose
Cel
l Sta
te
Tipping Point
Office of Research and DevelopmentNational Center for Computational Toxicology
Select Modeling Approach to Suit the Problem
• Estrogen receptor model: –Perturbing a single receptor is the first key step in adversity–Statistical, pathway-based, accounting for assay noise
• Cell-agent-based Virtual Embryo Model–Development is driven by complex cell-cell signaling –Agent-based model integrates time, dose, cell internals and signaling
• Tipping Point Model–Not all doses are adverse–Statistical model integrates multiple signals to find point of no return
32
Office of Research and DevelopmentNational Center for Computational Toxicology
Summary: Modeling Complex Biological Systems
• Our first goal is prediction• Predictions are based on models• “All models are wrong”• All models are based on data, which is always subject to noise, variability
• Therefore, all predictions are uncertain
“You have a big approximation and a small approximation. The big approximation is your approximation to the problem you want to solve. The small approximation is involved in getting the solution to the approximate problem.” Origin Unknown: Maybe Douglas Bates. Though he attributed it to George Box, Box denied it.
33
Office of Research and DevelopmentNational Center for Computational Toxicology
National Center for Computational Toxicology
34
NCCT StaffRusty ThomasKevin CroftonKeith HouckAnn RichardRichard JudsonTom KnudsenMatt Martin*Grace PatlewiczWoody SetzerJohn WambaughTony WilliamsSteve SimmonsChris GrulkeKatie Paul-FriedmanJeff EdwardsChad DeisenrothJoshua HarrillRebecca JolleyJeremy Dunne
NIH/NCATS CollaboratorsMenghang XiaRuili HuangAnton Simeonov
NTP CollaboratorsWarren CaseyNicole KleinstreuerMike DevitoDan ZangRick PaulesNisha Sipes
NCCT PostdocsTodor AntonijevicAudrey BoneSwapnil ChavanKristin Connors*Danica DeGrootJeremy FitzpatrickDustin Kapraun*Agnes Karmaus*Max Leung*Kamel Mansouri*Andrew McEachranLyLy PhamPrachi PradeepCaroline Ring*Kate SailiEric Watt*Todd Zurlinden
NCCTNancy BakerDayne Filer*Parth Kothiya*Sean WatfordIndira ThillainadarajahRobert PearceDanielle SuarezDoris SmithJamey VailRisa SayreNathan Rush
* Graduates