Transcriptome-wide changes associated with the reproductive behaviour of male guppies exposed to 17α-ethinyl estradiol.

Although many pharmaceutical compounds (and their metabolites) can induce harmful impacts at the molecular, physiological and behavioural levels, their underlying mechanistic associations have remained largely unexplored. Here, we utilized RNA-Seq to build a whole brain transcriptome profile to examine the impact of a common endocrine disrupting pharmaceutical (17α-ethinyl estradiol, EE2) on reproductive behaviour in wild guppies (Poecilia reticulata). Specifically, we annotated 16,791 coding transcripts in whole brain tissue in relation to the courtship behaviour (i.e. sigmoid display) of EE2 exposed (at environmentally relevant concentration of 8 ng/L for 28-days) and unexposed guppies. Further, we obtained 10,960 assembled transcripts matching in the non-coding orthologous genomes. Behavioural responses were assessed using a standard mate choice experiment, which allowed us to disentangle chemical cues from visual cues. We found that a high proportion of the RNAseq reads aligned back to our de novo assembled transcriptome with 80.59% mapping rate. Behavioural experiments showed that when males were presented only with female visual cues, there was a significant interaction between male treatment and female treatment in the time spent in the preference zone. This is one of the first studies to show that transcriptome-wide changes are associated with the reproductive behaviour of fish: EE2 exposed male guppies that performed high levels of courtship had a gene profile that deviated the most from the other treatment groups, while both non-courting EE2 and control males had similar gene signatures. Using Gene Ontology pathway analysis, our study shows that EE2-exposed males had gene transcripts enriched for pathways associated with altered immunity, starvation, altered metabolism and spermatogenesis. Our study demonstrates that multiple gene networks orchestrate courting behaviour, emphasizing the importance of investigating impacts of pharmaceuticals on gene networks instead of single genes.