Kristoffer Vitting-Seerup:
Transcriptional diversity and regulation across time and states

Date: 30-09-2016    Supervisor: Albin Sandelin

Originally the production of RNA copies from genes was thought to serve just as an intermediary step in the production of proteins. This view has however drastically changed with the emergence of several important functions of RNA. It has been found that the production of RNA also serves as to increase the functional output of our genomes via the production of multiple transcriptional isoforms from a single gene. Usage of different isoforms in different conditions, both within a single gene and at genome wide scales, is known to have substantial biological impact caused by the difference in functional potential of the isoforms. It is for example widely known that during maturation of male germ cells an essential feature is the usage of a set of shorter isoforms. Furthermore many changes of isoform usage in single genes, often referred to as isoform switches, are highly important in both health and disease and can for example by themselves drive tumor formation. Unfortunately many researches are generally not considering isoforms when analyzing gene expression data leaving a large gap in our understanding of transcriptional output.

Another recent finding is that an essential part of gene regulation, termed enhancers, produces RNA molecules when they are active. This has resulted in many studies offering tremendous insights into the regulatory mechanisms controlling gene expression. The studies have however almost exclusively analyzed steady states - meaning how a signal to start transcription is transferred through the regulatory levels is currently unknown.

In this thesis we have utilized high-throughput sequencing of RNA to perform genome wide analysis of transcriptional diversity and regulation across time and states. Specifically we have developed computational tools for both genome wide analysis of usage of isoforms (Article I) as well as for analysis of differential isoform usage in individual genes (Article IV). These tools have been used to extensively profile transcriptional diversity in both health and disease (Article II-IV) highlighting the importance of resolution analysis. Lastly we have used time-course data to perform an analysis of gene regulation in unprecedented details. The analysis resulted in a model where regulatory signals are deciphered first at enhancers and then subsequently in genes (Article V). This model, which is consistent across different stimuli and species, highlights the pivotal role of enhancers in the regulation of genes.