Classifying chemical mode of action using gene networks and machine learning: A case study with the herbicide linuron

详细信息    查看全文

• 作者：Anna Ornostay ; Andrew M. Cowie ; Matthew Hindle ; Christopher J.O. Baker ; Christopher J. Martyniuk
• 关键词：AR ; androgen receptor ; AXIN1 ; axin 1 ; BCL2L1 ; BCL2-like 1 ; BMPR ; bone morphogenetic protein receptor ; C ; Parameter of cost ; CSNK1E ; casein kinase 1 ; epsilon ; CTNNB1 ; catenin (cadherin-associated protein) ; beta 1 ; 88  ; kDa ; CYP17A1 ; cytochrome P450 ; family 17 ; subfamily A ; polypeptide 1 ; CYP1B1 ; cytochrome P450 ; family 1 ; subfamily B ; polypeptide 1 ; DHCR24 ; 24-dehydrocholesterol reductase ; DHT ; 5伪-dihydrotestosterone ; DVL1 ; disheveled ; dsh homolog 1 (Drosophila) ; E2 ; 17-尾 estradiol ; EDCs ; en
• 刊名：Comparative Biochemistry and Physiology Part D: Genomics and Proteomics
• 出版年：December, 2013
• 年：2013
• 卷：8
• 期：4
• 页码：263-274
• 全文大小：1263 K

文摘

The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5伪-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12 h. Ovaries exposed to DHT showed a significant increase in 17尾-estradiol (E₂) production while FLUT and LIN had no effect on E₂. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species.