T1000: a reduced gene set prioritized for toxicogenomic studies

PeerJ

article

Othman Soufan¹  Jessica Ewald²  Charles Viau¹  Doug Crump³  Markus Hecker⁴  Niladri Basu²  Jianguo Xia¹ 

[1] Institute of Parasitology, McGill University;Faculty of Agricultural and Environmental Sciences, McGill University;Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, National Wildlife Research Centre, Carleton University;School of the Environment & Sustainability and Toxicology Centre, University of Saskatchewan;Department of Animal Science, McGill University

关键词: Toxicogenomics;    Gene signature;    Co-expression network;    Graph clustering;    Machine learning;    Gene selection;   

DOI  :  10.7717/peerj.7975

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

【 授权许可】

CC BY