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Presentation for SETAC on Nov. 13-17, 2022 in Pittsburgh, PA Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Presentation to the Society of Environmental Toxicology and Chemistry (SETAC) North America 42nd Annual Meeting (November 2021)Search for CCTE records in EPA���s Science Inventory by typing in the title at this link.https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Presentation to the SETAC North America 42nd Annual Meeting (Nov. 2021)Search for CCTE records in EPA���s Science Inventory by typing in the title at this link.https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Presentation to the Society of Toxicology annual meeting March 2020

The Adverse Outcome Pathway framework is increasingly used to integrate data generated based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms.Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016 using terms from existing biological ontologies.AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from fourteen ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components.This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new approaches described here for annotating key events will also promote the visualization and analysis of genomics data in an AOP context.

Fundamental questions remain about the application of omics in environmental risk assessments, such as the consistency of data across laboratories. The objective of the present study was to determine the congruence of transcript data across 6 independent laboratories. Male fathead minnows were exposed to a measured concentration of 15.8 ng/L 17α‐ethinylestradiol (EE2) for 96 h. Livers were divided equally and sent to the participating laboratories for transcriptomic analysis using the same fathead minnow microarray. Each laboratory was free to apply bioinformatics pipelines of its choice. There were 12 491 transcripts that were identified by one or more of the laboratories as responsive to EE2. Of these, 587 transcripts (4.7%) were detected by all laboratories. Mean overlap for differentially expressed genes among laboratories was approximately 50%, which improved to approximately 59.0% using a standardized analysis pipeline. The dynamic range of fold change estimates was variable between laboratories, but ranking transcripts by their relative fold difference resulted in a positive relationship for comparisons between any 2 laboratories (mean R2 > 0.9, p 20‐fold; e.g., vitellogenin) to subtle (∼2‐fold; i.e., block of proliferation 1) were identified as differentially expressed, suggesting that laboratories can consistently identify transcripts that are known a priori to be perturbed by a chemical stressor. Thus, attention should turn toward identifying core transcriptional networks using focused arrays for specific chemicals. In addition, agreed‐on bioinformatics pipelines and the ranking of genes based on fold change (as opposed to p value) should be considered in environmental risk assessment. These recommendations are expected to improve comparisons across laboratories and advance the use of omics in regulations. Environ Toxicol Chem 2017;36:2593–2601. © 2017 SETAC

Relative transcriptomic impact of various treatment conditions in fathead minnow. Effects are measured by the percentages of transcriptome determined to be DEGs. (PPTX 39Â kb)

Cmap across ZF 21K and ZF 43K with the maximum signature size of 100 probes. (DOCX 14Â kb)

A network view of chemicals and tissue types based on samples profiled on ZF 21K. Each chemical treatment condition and the tissue profiled is represented as a node. Two nodes are connected by an edge weighted by their connectivity score. A shorter, darker, and wider edge between two nodes denote a higher connectivity score. All connections shown are statistically significant. A) 40 nodes with 573 connections; B) 30 nodes with 380 connections with each node having a minimum connectivity score of 10 in at least one of its connections. Tissue types: T, testis; O, ovary; B, brain; Lr, liver. (PDF 37Â kb)

Relationships between connectivity strength and DEGs. A total of 106 chemical conditions with relative transcriptomic impact ≥ 0.001 were considered. Connectivity scores were normalized to the size of respective query signatures. A) average LogFC vs connectivity scores; B) average logFC vs relative transcriptomic impact (RTI, percentage of transcriptome as DEGs); C) connectivity scores vs RTIs. (PPTX 49 kb)