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Download Computationally-predicted AOPs Shannon M. Bell Staff Toxicologist
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Computationally-predicted AOPs Shannon M. Bell Staff 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 Molecular Tissue Individual Overview of the AOP framework Omics/HTT Molecular Cellular Molecular initiating event (MIE) Chemical induced perturbations that affect biological systems at the molecular level. One MIE may lead to multiple AO Phenotyping Tissue Biomonitoring Organ Key events (KE) Intermediate effects or predictive associations spanning several levels of biological association KE are measurable Individual Population Adverse Outcome (AO) This may occur at the population level for ecological outcomes or at the individual level for human health outcomes 4 Adapted from Noffisat Oki AOa KE1 KE2 KE3 KE4 KE5 KE1 KE2 KE3 KE4 KE5 KE1 KE2 KE3 KE4 KE5 AOb KE1 KE2 KE3 KE4 KE5 AOc KE1 KE2 KE3 KE4 KE5 AOa AOb AOa AOc 5 AOP Discovery & Development <100s a year Putative AOPs computationallypredicted AOPs & networks AOP Networks ~10s a year Formal AOPs <10s a year Quantitative AOPs Adapted from Steve Edwards DE gene expression data Enriched pathways Data-generated connections Discretized data Annotated associations Inferred connections Phenotype node Expression node Phenotype data Other data Processing HTS data cpAOP network Chemical Adverse outcome High through put screening Bell et al, in submission 7 Adverse Outcomes Metabolic Extracellular dysregulation Lipid and metabolic dysregulation matrix (phenotype) Protein metabolism Cytotoxicity Phenotypes Chemicals Pathways inflammation Cell cycle DNA metabolism HTS Endpoints Stress response Cell signaling Hedgehog /wnt Cell division/ proliferation ERBB2/4/ AKT SREBP/ ChREBP activation Cancer MIE Lipid imbalance Fatty Liver Bell et al, in submission Direct support in cpAOP subset Possible support in cpAOP subset Weber, Boll and Stampfl 2003; Figure 3 9 Metabolic dysregulation Extracellular matrix Protein metabolism Lipid and metabolic dysregulation (phenotype) Cytotoxicity inflammation Cell cycle DNA metabolism Stress response Cell signaling Key event with ToxCast assay Energy Key event dysregulation Phenotype Wnt/Hedghog signaling ERBB2/4/Akt Cytotoxicity Ketone body metabolism ChREBP Apoptosis SREBP Interleukin/ interferon Cell cycle/ proliferation Extra cellular matrix organization Lipid dysregulation Inflammation Proliferation Cancer Angrish et al, in submission Inflammation Bell et al, in submission Lipid dysregulation Steatosis 11 AOa KE1 KE2 KE3 KE4 KE5 KE1 KE2 KE3 KE4 KE5 KE1 KE2 KE3 KE4 KE5 AOb KE1 KE2 KE3 KE4 KE5 AOc KE1 KE2 KE3 KE4 KE5 AOa AOb AOa So how do we start integrating the AOPs and cpAOPs in a computational manner? AOc 12 Ingest AOPs and Evidence REACH Tox21 Omics AOP Ontology Quantitative AOP-based Modeling AOP Network Analysis Causal AO Inference Assay Battery Identification Risk Values Risk Assessment/Management Slide courtesy of Lyle Burgoon Slide courtesy of Lyle Burgoon Slide courtesy of Lyle Burgoon Critical/sufficient node AOP node Burgoon, in submission (aop R package available on GitHub) 16 • Literature • HTS • Omics Data Expert refinement • Biological relevance • Assay targets Defining associations • Evidence integration • Leverage data mining methods cpAOP • Facilitate expert curations • Improve AOP development 17 Thank you Steve Edwards, US EPA Michelle Angrish, US EPA Charles Wood, US EPA Noffisat Oki, ORISE/US EPA Lyle Burgoon, US Army
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