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  • Computationally-predicted AOPs

    Shannon M. BellStaff Toxicologist

    Integrated Laboratory Systems

  • How do we go from a mouse, a chip/plate, or a poke to a population... Quickly, easily, cheaply, and transparently?

  • 3

    Mol

    ecul

    arTi

    ssue

    Indi

    vidu

    al

  • Overview of the AOP framework

    Molecular Cellular Tissue Organ Individual Population

    Omics/HTT Phenotyping Biomonitoring

    Molecular initiating event (MIE)

    Chemical induced perturbations that affect biological systems at the molecular level.One MIE may lead to

    multiple AO

    Key events (KE)Intermediate effects or predictive associations spanning several levels of biological associationKE are measurable

    Adverse Outcome (AO)This may occur at the population level for ecological outcomes or at the individual level for human health outcomes

    Adapted from Noffisat Oki4

  • 5

    KE1 KE2 KE3 KE4 KE5AOa

    AOb

    KE1 KE2 KE3 KE4 KE5AOc

    AOa

    KE1 KE2 KE3 KE4 KE5 AOa

    KE1 KE2 KE3 KE4 KE5 AOb

    KE1 KE2 KE3 KE4 KE5 AOc

  • AOP Discovery & Development

    Formal AOPs

    Quantitative AOPs

    Putative AOPs

    AOP Networks

    computationally-predicted AOPs &networks

  • Phenotype node

    Expression node

    High through put screening

    Data-generated connections

    Inferred connections

    Annotated associations

    DE gene expression data

    Phenotype data

    Enriched pathways

    Adverse outcome

    Chemical

    HTS data

    cpAOP network

    Discretized data

    Other data

    Processing

    Bell et al, in submission 7

  • Cell cycleDNA metabolism

    Protein metabolism

    Cell signaling

    Cytotoxicity inflammation

    Lipid and metabolic dysregulation (phenotype)

    Metabolic dysregulation

    Stress response

    Extracellular matrix

    MIE

    Cell division/ proliferation

    Hedgehog /wnt

    Cancer

    Fatty Liver

    SREBP/ ChREBP

    activationERBB2/4/

    AKTLipid

    imbalance

    Phenotypes

    PathwaysChemicals

    HTS Endpoints

    Adverse Outcomes

    Bell et al, in submission

  • 9Weber, Boll and Stampfl 2003; Figure 3

    Direct support in cpAOP subset

    Possible support in cpAOP subset

  • Cell cycleDNA metabolism

    Protein metabolism

    Cell signaling

    Cytotoxicity inflammation

    Lipid and metabolic dysregulation (phenotype)

    Metabolic dysregulation

    Stress response

    Extracellular matrix

  • 11Angrish et al, in submission

    Cancer

    Cell cycle/ proliferation

    ERBB2/4/Akt

    SteatosisInflammation

    Lipid dysregulation

    ChREBP

    SREBP

    Lipid dysregulation

    Extra cellular matrix

    organization

    Cytotoxicity

    ProliferationInflammation

    Ketone body metabolism

    Wnt/Hedghogsignaling

    Interleukin/ interferon

    Apoptosis

    Energy dysregulation

    Key event with ToxCast assayKey eventPhenotype

    Bell et al, in submission

  • KE1 KE2 KE3 KE4 KE5AOa

    AOb

    KE1 KE2 KE3 KE4 KE5AOc

    AOa

    KE1 KE2 KE3 KE4 KE5 AOa

    KE1 KE2 KE3 KE4 KE5 AOb

    KE1 KE2 KE3 KE4 KE5 AOc

    12So how do we start integrating the AOPs and cpAOPs in a computational manner?

  • Ingest AOPsand Evidence

    Quantitative AOP-based Modeling

    AOP Network AnalysisCausal AO Inference

    Assay Battery IdentificationRisk Values

    Risk Assessment/Management

    AOP Ontology

    Tox21OmicsREACH

    Slide courtesy of Lyle Burgoon

  • Slide courtesy of Lyle Burgoon

  • Slide courtesy of Lyle Burgoon

  • 16

    Critical/sufficient node

    AOP node

    Burgoon, in submission (aop R package available on GitHub)

  • Facilitate expert curations

    Improve AOPdevelopment

    Biological relevance

    Assay targets

    Evidence integration

    Leverage data mining methods

    Literature HTS Omics

    Data Defining associations

    cpAOPExpert refinement

    17

  • Thank you

    Steve Edwards, US EPAMichelle Angrish, US EPACharles Wood, US EPA

    Noffisat Oki, ORISE/US EPA

    Lyle Burgoon, US Army

    Title SlideHow do we go from a mouse, a chip/plate, or a poke to a population... Quickly, easily, cheaply, and transparently?Putting Information Into an AOP ContextOverview of the AOP frameworkIntegrating Multiple AOPsAOP Discovery & DevelopmentMaking of a Computationally Predicted AOP NetworkA Computationally Predicted AOP Subnetwork for Carbon TetrachlorideCarbon Tetrachloride-induced HepatotoxicityCPAOP Subnetwork for SteatosisComparison of CPAOP With Literature-based AOPIntegrating Multiple AOPsResources for AOP DevelopmentAOPXplorerFour AOPs for SteatosisCombined Steatosis AOPSlide Number 17Thank you

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