applying bioinformatics to chemical risk assessment...

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


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


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


Exposure

mg/kg BW/day

Hazard: Kinetics

+Dynamics

LowPriority

MediumPriority

HighPriority

Risk-based ApproachHazard + Exposure (+ uncertainty)

Kine

tics

Dyn

amic

s


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


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


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


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)


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


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


Schematic explanation of non-specific activity

13

Oxidative StressDNA ReactivityProtein ReactivityMitochondrial stress

ER stressCell membrane disruptionSpecific apoptosis…

Specific Non-specific



Dimerization

CofactorRecruitment

DNA Binding

RNA Transcription

Protein Production


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)


Assay

ER agonist pathway



R2

N7

ER Receptor Binding

(Antagonist)

A17

A18

Dimerization

N8

N9DNA Binding

CofactorRecruitment


R4

R9

A1

ATG TRANSATG CIS

Tox21 BLATox21 LUC

Tox21 BLATox21 LUC ACEA

OT PCA αα,αβ,ββ

OT Chromatin Binding

NVSbovinehumanmouse



Dimerization

CofactorRecruitment

DNA Binding

RNA Transcription

Protein Production


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


R4

R9

A1Receptor (Direct Molecular Interaction)


Assay

ER agonist pathway




Example chemicals:Observe quantitative uncertainty

17

True Agonist

Assay Interference Example “R3”


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


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


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


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 )


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 )


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


“Normal” State

Fluaz inam 0 .78 uM

0h

System trajectories


State Transit ion

Fluaz inam 0 .78 uM

0h

1h

System trajectories


System trajectory

Fluaz inam 0 .78 uM

0h

1h

24h24h

System trajectories


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


Fluazinam “Trajectories”

Increasing Dose

Cel

l Sta

te

Tipping Point


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


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


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

applying bioinformatics to chemical risk assessment...

Documents