Due to their involvement in dependence pathways, opioid system genes represent strong candidates for association studies investigating alcoholism. In this study, single nucleotide polymorphisms within the genes for mu (OPRM1) and kappa (OPRK1) opioid receptors and precursors of their ligands - proopiomelanocortin (POMC), coding for beta-endorphin and prodynorphin (PDYN) coding for dynorphins, were analyzed in a case-control study that included 354 male alcohol-dependent and 357 male control subjects from Croatian population. Analysis of allele and genotype frequencies of the selected polymorphisms of the genes OPRM1/POMC and OPRK1/PDYN revealed no differences between the tested groups. The same was true when alcohol-dependent persons were subdivided according to the Cloninger\'s criteria into type-1 and type-2 groups, known to differ in the extent of genetic control. Thus, the data obtained suggest no association of the selected polymorphisms of the genes OPRM1/POMC and OPRK1/PDYN with alcoholism in Croatian population.

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.