The Archaeal Elongation Factor EF-2 Induces the Release of aIF6 From 50S Ribosomal Subunit

On the origin of the nucleus: a hypothesis

SUMMARYIn this hypothesis article, we explore the origin of the eukaryotic nucleus. In doing so, we first look afresh at the nature of this defining feature of the eukaryotic cell and its core functions-emphasizing the utility of seeing the eukaryotic nucleoplasm and cytoplasm as distinct regions of a common compartment. We then discuss recent progress in understanding the evolution of the eukaryotic cell from archaeal and bacterial ancestors, focusing on phylogenetic and experimental data which have revealed that many eukaryotic machines with nuclear activities have archaeal counterparts. In addition, we review the literature describing the cell biology of representatives of the TACK and Asgardarchaeaota - the closest known living archaeal relatives of eukaryotes. Finally, bringing these strands together, we propose a model for the archaeal origin of the nucleus that explains much of the current data, including predictions that can be used to put the model to the test.

Integrating Phylogenetics With Intron Positions Illuminates the Origin of the Complex Spliceosome

Eukaryotic genes are characterized by the presence of introns that are removed from pre-mRNA by a spliceosome. This ribonucleoprotein complex is comprised of multiple RNA molecules and over a hundred proteins, which makes it one of the most complex molecular machines that originated during the prokaryote-to-eukaryote transition. Previous works have established that these introns and the spliceosomal core originated from self-splicing introns in prokaryotes. Yet, how the spliceosomal core expanded by recruiting many additional proteins remains largely elusive. In this study, we use phylogenetic analyses to infer the evolutionary history of 145 proteins that we could trace back to the spliceosome in the last eukaryotic common ancestor. We found that an overabundance of proteins derived from ribosome-related processes was added to the prokaryote-derived core. Extensive duplications of these proteins substantially increased the complexity of the emerging spliceosome. By comparing the intron positions between spliceosomal paralogs, we infer that most spliceosomal complexity postdates the spread of introns through the proto-eukaryotic genome. The reconstruction of early spliceosomal evolution provides insight into the driving forces behind the emergence of complexes with many proteins during eukaryogenesis.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.