Tempo and mode of gene duplication in mammalian ribosomal protein evolution.

Gene duplication has been widely recognized as a major driver of evolutionary change and organismal complexity through the generation of multi-gene families. Therefore, understanding the forces that govern the evolution of gene families through the retention or loss of duplicated genes is fundamentally important in our efforts to study genome evolution. Previous work from our lab has shown that ribosomal protein (RP) genes constitute one of the largest classes of conserved duplicated genes in mammals. This result was surprising due to the fact that ribosomal protein genes evolve slowly and transcript levels are very tightly regulated. In our present study, we identified and characterized all RP duplicates in eight mammalian genomes in order to investigate the tempo and mode of ribosomal protein family evolution. We show that a sizable number of duplicates are transcriptionally active and are very highly conserved. Furthermore, we conclude that existing gene duplication models do not readily account for the preservation of a very large number of intact retroduplicated ribosomal protein (RT-RP) genes observed in mammalian genomes. We suggest that selection against dominant-negative mutations may underlie the unexpected retention and conservation of duplicated RP genes, and may shape the fate of newly duplicated genes, regardless of duplication mechanism.

SCLD: a Stem Cell Lineage Database for the annotation of cell types and developmental lineages.

Stem cell biology has experienced explosive growth over the past decade as researchers attempt to generate therapeutically relevant cell types in the laboratory. Recapitulation of endogenous developmental trajectories is a dominant paradigm in the design of directed differentiation protocols, and attempts to guide stem cell differentiation are often based explicitly on knowledge of in vivo development. Therefore, when designing protocols, stem cell biologists rely heavily upon information including (i) cell type-specific gene expression profiles, (ii) anatomical and developmental relationships between cells and tissues and (iii) signals important for progression from progenitors to target cell types. Here, we present the Stem Cell Lineage Database (SCLD) (http://scld.mcb.uconn.edu) that aims to unify this information into a single resource where users can easily store and access information about cell type gene expression, cell lineage maps and stem cell differentiation protocols for both human and mouse stem cells and endogenous developmental lineages. By establishing the SCLD, we provide scientists with a centralized location to organize access and share data, dispute and resolve contentious relationships between cell types and within lineages, uncover discriminating cell type marker panels and design directed differentiation protocols.