Landscape and evolution of tissue-specific alternative polyadenylation across Drosophila species - Piero Sanfilippo; Jiayu Wen; Eric C. Lai (Genome Biology, 2017)
Abstract Background Drosophila melanogaster has one of best-described transcriptomes of any multicellular organism. Nevertheless, the paucity of 3′-sequencing data in this species precludes comprehensive assessment of alternative polyadenylation (APA), which is subject to broad tissue-specific control. Results Here, we generate deep 3′-sequencing data from 23 developmental stages, tissues, and cell lines of D. melanogaster, yielding a comprehensive atlas of ~ 62,000 polyadenylated ends. These data broadly extend the annotated transcriptome, identify ~ 40,000 novel 3′ termini, and reveal that two-thirds of Drosophila genes are subject to APA. Furthermore, we dramatically expand the numbers of genes known to be subject to tissue-specific APA, such as 3′ untranslated region (UTR) lengthening in head and 3′ UTR shortening in testis, and characterize new tissue and developmental 3′ UTR patterns. Our thorough 3′ UTR annotations permit reassessment of post-transcriptional regulatory networks, via conserved miRNA and RNA binding protein sites. To evaluate the evolutionary conservation and divergence of APA patterns, we generate developmental and tissue-specific 3′-seq libraries from Drosophila yakuba and Drosophila virilis. We document broadly analogous tissue-specific APA trends in these species, but also observe significant alterations in 3′ end usage across orthologs. We exploit the population of functionally evolving poly(A) sites to gain clear evidence that evolutionary divergence in core polyadenylation signal (PAS) and downstream sequence element (DSE) motifs drive broad alterations in 3′ UTR isoform expression across the Drosophila phylogeny. Conclusions These data provide a critical resource for the Drosophila community and offer many insights into the complex control of alternative tissue-specific 3′ UTR formation and its consequences for post-transcriptional regulatory networks.