aop based ontologies for developmental toxicity
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AOP-based ontologies for developmental toxicity Thomas B. Knudsen, - PowerPoint PPT Presentation

EUROTOX 2018, Brussels Symposium: Adverse Outcome Pathways and Development of Alternative Methods AOP-based ontologies for developmental toxicity Thomas B. Knudsen, PhD Developmental Systems Biologist US EPA, National Center for


  1. EUROTOX 2018, Brussels Symposium: “Adverse Outcome Pathways and Development of Alternative Methods” AOP-based ontologies for developmental toxicity Thomas B. Knudsen, PhD Developmental Systems Biologist US EPA, National Center for Computational Toxicology knudsen.thomas@epa.gov ORCID 0000-0002-5036-596x DISCLAIMER: The views expressed are those of the presenter and do not necessarily reflect Agency policy.

  2. Vascular Development Blood vessel development is essential to the embryo (cardiovascular is first • functioning organ system across Vertebrate species). Vascular insufficiency is tied to many disease processes (stroke, diabetes, pre- • eclampsia, neonatal respiratory distress, osteoporosis, Alzheimer’s…). • Aop43: one of 28 AOPs included in the OECD work plan with status ‘open for citation & comment’ [https://aopwiki.org/wiki/index.php/Aop:43] . 2

  3. AOP framework: developmental vascular toxicity (DVT) Vasculogenesis Primary tubular network Angiogenesis Remodeling SOURCE: Knudsen and Kleinstreuer (2011) Birth Defects Res 3

  4. AOP-based ranking: predicted vascular disrupting chemicals (pVDCs) 24 ToxCast target assays 1058 ToxCast chemicals ranked by pVDC ToxPi (pVDC ToxPi) (38 circled for validation) SOURCE: Kate Saili, NCCT 4

  5. VEGFR2 inhibition (PTK787) ISV length (72 hpf) malformation Terata (120 hpf) mortality Lifespan (10 dpf) 5 SOURCE: Tal et al. (2014) Reprod Toxicol 5 5

  6. Vasculogenesis How well does ToxCast do predicting endothelial disruption across the angiogenesis cycle? Primary tubular network • 3D angiogenic sprouting [Belair et al. (2016) Acta Biomat] • nuCTNB and endothelial migration [in preparation] • HTS tubulogenesis [Li et al. (2018) SLAS Tech] • endothelial co-culture [in preparation] Angiogenesis • engineered matrices [Nguyen et al. (2017) Nature Bioeng] • KDR-reporter zebrafish embryos [Tal et al. (2017) Reprod Toxicol] • rat whole embryo culture [Ellis-Hutchings et al. (2017) Reprod Toxicol] Remodeling 6

  7. 38 chemical test set: qualification of pVDC ToxPi across 9 endothelial behaviors A B C D E F G H I J K L A pVDC ToxPi Sprouting UWisc tubulohenesis ZF-TG embryo ToxCast pVDC tubulogenesis tubulogenesis tubulogenesis tubulogenesis EC Migration B HUVEC tubulogenesis (FICAM) ZF hyaloid synthetic Matrigel nuCTNB FICAM NCATS VALA ANY C HUVEC tubulogenesis (NCATS) Decane 0 0 1,2,3-Trichloropropane 0 0 D tubulogenesis in synthetic matrices (HMAPS) Pymetrozine 0 0 Methimazole 0 0 Imazamox 0 0 E tubulogenesis in Matrigel (HMAPS) D-Mannitol 0 0 Methylparaben 0 0 Valproic acid 0 0 F nuCTNB biomarker (VALA) Tris(2-ethylhexyl) phosphate 0 0 PFOS 0 0 TNP-470 0 1 G endothelial cell migration (VALA) 4-Nonylphenol, branched 0 1 1,2,4-Trichlorobenzene 0 2 Diethanolamine 0 2 H iPSC endothelial sprouting (HMAPS) Reserpine 0 2 Sodium dodecylbenzenesulfonate 0 2 Oxytetracycline dihydrate 0 2 Quercetin I ISV reporter zebrafish (NHEERL) 0 2 Tris(2-chloroethyl) phosphate 0 3 2,4-Diaminotoluene 0 3 Tris(1,3-dichloro-2-propyl)phosphate 0 3 J reporter zebrafish (UDUBLIN) Celecoxib 0 3 C.I. Solvent Yellow 14 0 3 tert-Butylhydroquinone 0 4 K HUVEC tubulogenesis (VALA) Triclosan 0 4 Bisphenol AF 0 4 Haloperidol 0 4 L ANY (B to K) Docusate sodium 0 5 Cladribine 0 5 Triclocarban 0 5 Pyridaben 0 5 1-Hydroxypyrene 0 5 Disulfiram 0 5 Sens 0.89, Spec 0.80 Bisphenol A 0 5 Fluazinam 0 6 Phenolphthalein 0 6 Octyl gallate ACC 87% (PPV 93%, NPV 73%) 0 6 5HPP-33 0 8 7

  8. Embryotoxicity: 5HPP-33 vs TNP-470 5HPP-33 TNP-470 • synthetic thalidomide analog • synthetic fumagillin analog • microtubule disruptor • MetAP II inhibitor • ↓ endothelial networks • non-canonical WNT signaling • critical effect - embryo viability • critical effect - dysmorphogenesis • AC50 = 21.2 µM • AC50 = 0.038 µM • TI threshold from hESC = 9.5 µM • TI threshold from hESC = 0.01 µM 8 SOURCE: Ellis-Hutchings et al. (2017) Reprod Toxicol

  9. RNAseq: 5HPP-33 vs TNP-470 whole embryo culture 2831 DEGs overlap SOM (464 genes in ROI box) ROI clusters • splicesome and RNA metabolism • protesosome and ubiquitination • FXR and LXR pathways common to 5HPP-33 and TNP-470 response. • FXR (+) and LXR (-) pathways may be key events via RXR heterodimerization. SOURCE: K Saili, J Franzosa (collaboration with DOW Chemical) 9

  10. Computer simulation: cell agent-based models Network assembly VEGF corridors VEGF165 MMPs VEGF121 sFlit1 TIE2 CXCL10 CCL2 Li and Carmeliet (2018) Science Nicole Kleinstreuer Kleinstreuer et al. (2013) PLoS Comp Biol SOFTWARE: www.CompuCell3D.org 10 BioComplexity Institute, Indiana U

  11. Simulated ( in silico ) profiling control  VEGF Imazamox Bisphenol A   PFOS  Fluazinam  Octyl gallate Pyridaben   Disulfiram 5HPP-33   11

  12. Neural tube vascularization Tata et al. (2015) Mechanism Devel VEGF-A gradient: NPCs in subventricular zone Microglial-Endothelial network endothelial tip cell endothelial stalk cell microglial cell 12 SOURCE: Zurlinden et al. (2018), NCCT 12

  13. Simulated dose-response: brain angiogenesis from in vitro HTS data (ToxCast) 0.03 µM CompTox Chemicals Dashboard 0.3 µM 2.0 µM 6.0 µM https://www.epa.gov/chemical-research/toxcast-dashboard 13

  14. Biomimetic reconstruction (hNVU) Computational prediction (cNVU) Critical concentration: • predicted in silico ~0.5 µM • observed in vitro ~0.3 µM W Murphy, W Daly, G Kaushick – U Wisconsin (HMAPS) Todd Zurlinden, Kate Saili - NCCT 14

  15. Summary: decoding the toxicological blueprint of vascular development • HTS profiles can assess in vitro bioactivity of large numbers of chemicals but translation remains a challenge for complex processes such as DevTox. • Mapping HTS features to AOPs brings into context the weight of evidence for critical determinants potential invoking the altered phenotype in a self-organizing system. • AOP-based ontologies provide the necessary structure for quantitative prediction of cellular and tissue responses to molecular perturbation. • The ‘angiogenic cycle’ is responsive to genetic and physiological signals in the embryonic microenvironment, and can be useful for predictive toxicology. • For DevTox, this can be demonstrated by an AOP network for embryonic vascular disruption represented in the OECD AOP-KB (Aop43) . 15

  16. Acknowledgements o Nicole Kleinstreuer - NCCT (now NTP/NICEATM) o Richard Spencer – EMVL o Nancy Baker – Leidos / NCCT o Jill Franzosa – NCCT (now CSS/NHEERL) o Ed Carney † – Dow Chemical Company o Rob Ellis-Hutchings – Dow Chemical Company o Raj Settivari – Dow Chemical Company o Tuula Heinonen – U Tampere / FICAM o Tarja Tomela – U Tampere / FICAM o Maria Bondesson – U Houston (TIVS) (now Indiana U) o James Glazier – Indiana U (TIVS) o Kate Saili – NCCT o Todd Zurlinden – NCCT o BeiBei Cai – Vala Sciences o Jill Franzosa – NCCT (now CSS) o Eric Nguyen – U Wisconsin (HMAPS) o Guarav Kaushick – U Wisconsin (HMAPS) o William Murphy – U Wisconsin (HMAPS) o William Daly – U Wisconsin (HMAPS) o Tamara Tal – NHEERL/ISTD o David Belair – NHEERL/TAD (now CellGene) o Florent Ginhoux – A*STAR/SIgN o Aymeric Silvin – A*STAR/SIgN 16

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