The role of alternative polyadenylation in cancer progression

MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer

Maturation of eukaryotic pre-mRNAs via splicing and polyadenylation is modulated across cell types and conditions by a variety of RNA-binding proteins (RBPs). Although there exist over 1,500 RBPs in human cells, their binding motifs and functions still remain to be elucidated, especially in the complex environment of tissues and in the context of diseases. To overcome the lack of methods for the systematic and automated detection of sequence motif-guided pre-mRNA processing regulation from RNA sequencing (RNA-Seq) data we have developed MAPP (Motif Activity on Pre-mRNA Processing). Applying MAPP to RBP knock-down experiments reveals that many RBPs regulate both splicing and polyadenylation of nascent transcripts by acting on similar sequence motifs. MAPP not only infers these sequence motifs, but also unravels the position-dependent impact of the RBPs on pre-mRNA processing. Interestingly, all investigated RBPs that act on both splicing and 3' end processing exhibit a consistently repressive or activating effect on both processes, providing a first glimpse on the underlying mechanism. Applying MAPP to normal and malignant brain tissue samples unveils that the motifs bound by the PTBP1 and RBFOX RBPs coordinately drive the oncogenic splicing program active in glioblastomas demonstrating that MAPP paves the way for characterizing pre-mRNA processing regulators under physiological and pathological conditions.

Development of Nanopore sequencing-based full-length transcriptome database toward functional genome annotation of the Pacific oyster, Crassostrea gigas

The Pacific oyster (Crassostrea gigas) is a widely cultivated shellfish in the world, while its transcriptome diversity remains less unexplored due to the limitation of short reads. In this study, we used Oxford Nanopore sequencing to develop the full-length transcriptome database of C. gigas. We identified 77,920 full-length transcripts from 21,523 genes, and uncovered 9668 alternative splicing events and 87,468 alternative polyadenylation sites. Notably, a total of 16,721 novel transcripts were annotated in this work. Furthermore, integrative analysis of 25 publicly available RNA-seq datasets revealed the transcriptome diversity involved in post-transcriptional regulation in C. gigas. We further developed a Drupal based webserver, Cgtdb, which can be used for transcriptome visualization, sequence alignment, and functional genome annotation analyses. This work provides valuable resources and a useful tool for integrative analysis of various transcriptome datasets in C. gigas, which will serve as an essential reference for functional annotation of the oyster genome.

Estimating the predictive power of silent mutations on cancer classification and prognosis

In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.