Session 7: Human Toxome project

Mapping Pathways of Toxicity by systems toxicology: The Human Toxome project
PRESENTING AUTHOR: 

Dr Mounir Bouhifd

INSTITUTION / COMPANY : 

Johns Hopkins Center for Alternatives to Animal Testing

POSITION: 

Research Associate

ABSTRACT CONTENT / DETAILS: 

The NRC report from 2007 "Toxicity Testing in the 21st Century: A vision and a strategy" (Tox-21c) has created an atmosphere of departure suggesting to move to a new resolution, i.e. pathways of toxicity (PoT).

The NIH Transformative Research Project “Mapping the Human Toxome by Systems Toxicology” represents an initiative promoting the implementation of a new toxicity testing paradigm. This collaboration of Johns Hopkins Bloomberg School of Public Health, Brown University, The Hamner Institute, Georgetown University, U.S. EPA National Center for Computational Toxicology and Agilent, aims to establish a workflow for the identification and annotation of pathways of toxicity (PoT).

The project uses untargeted mass-spectrum-based metabolomics and gene array-based transcriptomics to assess the proestrogenic response of MCF-7 cells, a well‐established and pre-validated test for endocrine disruption.

The standardized model SOP was established in two laboratories, which was monitored by diverse technologies: omics, karyotyping and Comparative Genomic Hybridization.

Time- and dose-dependent effects of different agonists are assessed using hundreds of gene arrays and thousands of metabolomics profiles representing different concentrations and time points of treatment.

Dedicated tools for the combined analysis of multi-omics data, their integration and visualization are developed and utilized within the consortium.

A number of challenges include the practical implementation of the conceptual framework of PoT, the quality and standardization of toxicological test systems and omics technologies and the bioinformatics tools for identification, annotation, proof of causality and validation of PoTs. Multi-omics data integration, network analysis tools and text-mining methodologies help pathways of toxicity identification.