title: Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.
abstract: 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 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-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.
authors.affiliation: Department of Biology and Canadian Rivers Institute, University of New Brunswick, Saint John, New Brunswick, E2L 4L5, Canada.
authors.forename: Anna
authors.initials: A
authors.lastname: Ornostay
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Cowie
authors.forename: Matthew
authors.initials: M
authors.lastname: Hindle
authors.forename: Christopher J O
authors.initials: CJ
authors.lastname: Baker
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Martyniuk
mesh_terms: D000726:Androgen Antagonists / Q000494:pharmacology*; D000818:Animals; D001185:Artificial Intelligence; D003530:Cyprinidae; D013196:Dihydrotestosterone / Q000494:pharmacology; D052244:Endocrine Disruptors / Q000494:pharmacology*; D004958:Estradiol / Q000096:biosynthesis; D005260:Female; D005485:Flutamide / Q000494:pharmacology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks / Q000187:drug effects*; D008044:Linuron / Q000494:pharmacology*; D010053:Ovary / Q000187:drug effects; D011944:Receptors, Androgen / Q000187:drug effects*; D015398:Signal Transduction; D060388:Support Vector Machine; D014874:Water Pollutants, Chemical / Q000494:pharmacology*
chemical_list: D000726:Androgen Antagonists; D052244:Endocrine Disruptors; D011944:Receptors, Androgen; D014874:Water Pollutants, Chemical; D008044:Linuron; D013196:Dihydrotestosterone; D004958:Estradiol; D005485:Flutamide
keywords: 17-β estradiol; 17beta-trenbolone; 24-dehydrocholesterol reductase; 5α-dihydrotestosterone; AR; AXIN1; BCL2-like 1; BCL2L1; BMPR; C; CSNK1E; CTNNB1; CYP17A1; CYP1B1; DHCR24; DHT; DVL1; E(2); EDCs; ER; FABP1; FDX1; FGF8; FHM; FLUT; FN1; FOS; FOXO; FRAT1; FSHB; FZD; GH1; GO; GSEA; GSK3B; Gene Ontology; GnRH; HNF1 homeobox B; HNF1B; Herbicides; ID1; IFT88; IL16; INPP5D; JUN; LDLR; LEFTY2; LIN; LRP6; MMP9; MOA; MYC; Machine learning; NUCB2; Notch signaling; PCK2; PCR; PCSK2; POMC; Parameter of cost; RBM; Radial Basis Machine; SEMA3A; SHBG; SLC5A1; SNEA; SOCS3; STAT; SVM; Sub-network enrichment analysis; Support Vector Machine; TB; TFE3; TGFBR; TH; Vtg; Wnt-frizzled pathway; androgen receptor; axin 1; bone morphogenetic protein receptor; casein kinase 1, epsilon; catenin (cadherin-associated protein), beta 1, 88kDa; cytochrome P450, family 1, subfamily B, polypeptide 1; cytochrome P450, family 17, subfamily A, polypeptide 1; disheveled, dsh homolog 1 (Drosophila); endocrine disrupting compounds; estrogen receptor; fathead minnow; fatty acid binding protein 1, liver; ferredoxin 1; fibroblast growth factor 8 (androgen-induced); fibronectin 1; flutamide; follicle stimulating hormone, beta polypeptide; forkhead box O3; frequently rearranged in advanced T-cell lymphomas; frizzled receptor; gene set enrichment analysis; glycogen synthase kinase 3 beta; gonadotropin-releasing hormone receptor; growth hormone 1; inhibitor of DNA binding 1, dominant negative helix–loop–helix protein; inositol polyphosphate-5-phosphatase, 145kDa; interleukin 16 (lymphocyte chemoattractant factor); intraflagellar transport 88 homolog (Chlamydomonas); jun oncogene; left–right determination factor 2; linuron; low density lipoprotein receptor; low density lipoprotein receptor-related protein 6; matrix metallopeptidase 9 (gelatinase B,92kDa gelatinase,92kDa type IV collagenase); mode of action; nucleobindin 2; phosphoenolpyruvate carboxykinase 2 (mitochondrial); polymerase chain reaction; proopiomelanocortin; proprotein convertase subtilisin/kexin type 2; sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3A; sex hormone-binding globulin; signal transducer and activator of transcription; solute carrier family 5 (sodium/glucose cotransporter), member 1; sub-network enrichment analysis; suppressor of cytokine signaling 3; transcription factor binding to IGHM enhancer 3; transforming growth factor, beta receptor 1; tyrosine hydroxylase; v-fos FBJ murine osteosarcoma viral oncogene homolog; v-myc myelocytomatosis viral oncogene homolog (avian); vitellogenin