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Biology subjects

Yi, H.

Publications and source records attributed to Yi, H..

2 recordsLinked to original sources

Negative Cooperativity between Gemin2 and RNA Determines RNA Selection and Release of the SMN Complex in snRNP Assembly

The assembly of snRNP cores, in which seven Sm proteins, D1/D2/F/E/G/D3/B, form a ring around the nonameric Sm site of snRNAs, is the early step of spliceosome formation and essential to eukaryotes. It is mediated by the PMRT5 and SMN complexes sequentially in vivo. SMN deficiency causes neurodegenerative disease spinal muscular atrophy (SMA). How the SMN complex assembles snRNP cores is largely unknown, especially how the SMN complex achieves high RNA assembly specificity and how it is released. Here we show, using crystallographic and biochemical approaches, that Gemin2 of the SMN complex enhances RNA specificity of SmD1/D2/F/E/G via a negative cooperativity between Gemin2 and RNA in binding SmD1/D2/F/E/G. Gemin2, independent of its N-tail, constrains the horseshoe-shaped SmD1/D2/F/E/G from outside in a physiologically relevant, narrow state, enabling high RNA specificity. Moreover, the assembly of RNAs inside widens SmD1/D2/F/E/G, causes the release of Gemin2/SMN allosterically and allows SmD3/B to join. The assembly of SmD3/B further facilitates the release of Gemin2/SMN. This is the first to show negative cooperativity in snRNP assembly, which provides insights into RNA selection and the SMN complexs release. These findings reveal a basic mechanism of snRNP core assembly and facilitate pathogenesis studies of SMA.

molecular biology

The Cell Cycle Browser: an interactive tool for visualizing, simulating, and perturbing cell cycle progression

SUMMARYThe cell cycle is driven by precise temporal coordination among many molecular activities. To understand and explore this process, we developed the Cell Cycle Browser (CCB), an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, simulating, and predicting the outcomes of perturbing cell cycle parameters. Time-series traces from individual cells can be combined to build a multi-layered timeline of molecular activities. Users can simulate the cell cycle using computational models that capture the dynamics of molecular activities and phase transitions. By adjusting individual expression levels and strengths of molecular relationships, users can predict effects on the cell cycle. Virtual assays, such as growth curves and flow cytometry, provide familiar outputs to compare cell cycle behaviors for data and simulations. The CCB serves to unify our understanding of cell cycle dynamics and provides a platform for generating hypotheses through virtual experiments.\n\nHIGHLIGHTSO_LIUsers can stack and align single-cell traces for different molecular reporters\nC_LIO_LIComputational models with adjustable parameters simulate cell cycle progression\nC_LIO_LIVirtual growth curves and flow cytometry assays predict cell cycle behaviors\nC_LI

systems biology