Poster: Computational prediction of AOPs

Integrative data mining of high-throughput in vitro screens, in vivo data, and disease information to identify Adverse Outcome Pathway (AOP) signatures

Oki, Noffisat1,2 and Edwards, Stephen2
1 Oak Ridge Institute for Science and Education, 2 U.S. Environmental Protection Agency, RTP, NC 27711, USA


Noffisat Oki PhD, Oak Ridge Institute for Science and Education

Noffisat Oki: Computational prediction of AOPs

ToxCast screening data and Comparative Toxicogenomics Database

The Adverse Outcome Pathway (AOP) framework provides a systematic way to describe linkages between molecular and cellular processes and organism or population level effects. The current AOP assembly methods however, are inefficient. Our goal is to generate computationally-predicted AOPs (cpAOPs) via data mining to accelerate AOP assembly and provide a more comprehensive coverage of biological space.

We used Frequent Itemset Mining (FIM) to find associations between the gene targets of ToxCast high-throughput screening (HTS) assays and disease phenotypes from the Comparative Toxicogenomics Database (CTD). ToxCast chemicals were used as aggregating variables for analyses.

A cpAOP network was defined by considering genes and diseases as nodes and FIM associations as edges, thereby providing a graphical representation of the links and highlighting indirect associations.

We illustrate an indirect association between AHR and glaucoma, suggesting a putative relationship. Though AHR isn’t found in a CTD gene-disease query for glaucoma, it is a regulator of CYP1B1 (not screened in ToxCast) and the incorporation of additional datasets enabled detection of the putative relationship. This example highlights the value in integrating multiple data sources when defining cpAOPs for HTS data.

The views expressed in this abstract are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA