Human Stem Cell-Based Platform to Predict Selective Developmental NeurotoxicityEPA Grant Number: R835552
Title: Human Stem Cell-Based Platform to Predict Selective Developmental Neurotoxicity
Investigators: Terskikh, Alexey V.
Current Investigators: Terskikh, Alexey V. , Farhy, Chen
Institution: Sanford-Burnham Medical Research Institute
EPA Project Officer: Lasat, Mitch
Project Period: September 1, 2013 through August 31, 2017
Project Amount: $800,000
RFA: Development and Use of Adverse Outcome Pathways that Predict Adverse Developmental Neurotoxicity (2012) RFA Text | Recipients Lists
Research Category: Health , Human Health , Safer Chemicals
Based on the epidemiological studies and our preliminary data we hypothesized that many environmental toxins are likely to preferentially affect neural development at the stage of neural precursors resulting in a selective loss of their cognate derivatives (e.g. neurons) in the adult brain. Capturing such selective adverse outcomes, selective developmental neurotoxicity (DNT), which cannot be achieved using conventional cytotoxicity assays, is our key objective. We propose to develop and implement a novel high throughput screen (HTS) of ToxCast chemicals using a human embryonic stem cell (hESC) model of neuronal development to identify adverse outcome pathways (AOPs) that lead to and predict DNT. Specifically, we propose to: 1. Develop an HTS platform based on hESC-derived Neural Precursor Cells (NPCs) to identify ToxCast phase I chemicals that selectively affect ventral or dorsal NPCs; 2. Adapt the human NPC-based assays to 384-well format and identify ToxCast phase II chemicals selectively affecting ventral or dorsal NPCs; 3. Investigate the cellular and molecular mechanisms of action of active ToxCast chemicals.
We expect to deliver quantitative measurements of selective (as well as general) DNT of the ToxCast chemicals and correlate their toxicity towards ventral/dorsal NPCs with their activity in a large number of ToxCast biochemical assay. On the other hand the toxicity dataset obtained using hESC-derived NPCs model will be analyzed in combination with ToxRefDB and other ToxCast resources to determine whether selective toxicity for NPCs can predict DNT outcomes in vivo. Highly active ToxCat chemicals identified here could be tested in animal models of DNT. Finally, identification of selective toxicity towards ventral vs dorsal NPCs will help guiding future epidemiological studies focusing efforts on relevant group of chemicals and pathological conditions.
The knowledge obtained in the course of the proposed project will significantly contribute to our understanding of the cellular and molecular mechanisms of DNT and will help to develop the next generation of safe agricultural and industrial chemicals.