Novo Nordisk Fonden - Challenge Programme 2018 - Function, structure, regulation and targeting of exosome adaptor complexes

Regulation and fidelity of gene expression is fundamental to the differentiation and maintenance of all living organisms. A central player here is the ribonucleolytic RNA exosome complex, which is involved in the processing and complete turnover of virtually all transcripts types. The core complex of the exosome has been comprehensively studied, revealing that it is merely a general RNA degradation machine. Instead, specific recognition of its multitude of substrates is achieved by the association of the exosome with so-called protein adaptor complexes. Although some of these have been identified, we do not understand how they recognize and discriminate their substrates, how they interact with the exosome and how these interactions are regulated to achieve adequate RNA turnover at steady state or in dynamic cellular settings. Furthermore, knowledge of the full spectrum of exosome adaptor complexes and their compositions is lacking. Finally, given the fundamental position of the exosome in cellular RNA biology, it is not surprising that the system is subject to intense targeting by cellular pathogens. The present Exo-Adapt initiative aims at understanding the human exosome adaptors both in depth and in breadth. Capitalizing on the standing of Exo-Adapt participants as world-leading experts in RNA exosome biology, it is our vision to i) characterize biochemically, structurally and functionally how adaptor complexes interact with the mammalian exosome to ‘feed’ it with specific RNA substrates, ii) identify new mammalian exosome adaptor complexes and describe their protein constituents and protein-protein interactions at the atomic level, and iii) place our findings in the context of exosome-pathogen interactions. In doing so, we will educate MSc, PhD and postdoctoral students to become new leaders of academic or industrial activities, by collaborating across the fields of structural, functional and computational biology.