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Frase, S.

Publications and source records attributed to Frase, S..

2 recordsLinked to original sources

Siah2 integrates mitogenic and extracellular matrix signalslinking neuronal progenitor ciliogenesis with germinal zoneoccupancy.

Evidence is lacking as to how developing neurons integrate mitogenic signals with microenvironment cues to control proliferation and differentiation. We determined that the Siah2 E3 ubiquitin ligase functions in a coincidence detection circuit linking responses to the Shh mitogen and the extracellular matrix to control cerebellar granule neurons (CGN) germinal zone (GZ) occupancy. We found that Shh maintains Siah2 expression in CGN progenitors (GNPs) in a Ras/Mapk-dependent manner. Siah2 supports ciliogenesis in a feed-forward fashion by restraining ciliogenic targets. Efforts to identify GZ sources of Ras/Mapk signaling led us to discover that GNPs respond to laminin, but not vitronectin, in the microenvironment via integrin {beta}1 receptors, which engages the Ras/Mapk cascade, and that this niche interaction is essential for promoting GNP ciliogenesis. As GNPs leave the GZ, differentiation is seamlessly driven by changing extracellular cues that diminish Siah2-activity leading to primary cilia retraction and attenuation of mitogenic responses.

neuroscience↗

The Nucleome of Developing Murine Rod Photoreceptors

The nuclei of rod photoreceptors in mice and other nocturnal species have an unusual inverted chromatin structure: the heterochromatin is centrally located to help focus light and improve photosensitivity. To better understand this unique nuclear organization, we performed ultra-deep Hi-C analysis on murine retina at 3 stages of development and on purified rod photoreceptors. Predicted looping interactions from the Hi-C data were validated with fluorescence in situ hybridization (FISH). We discovered that a subset of retinal genes that are important for retinal development, cancer, and stress response are localized to the facultative heterochromatin domain. We also used machine learning to develop an algorithm based on our chromatin Hidden Markov Modeling (chromHMM) of retinal development to predict heterochromatin domains and study their dynamics during retinogenesis. FISH data for 264 genomic loci were used to train and validate the algorithm. The integrated data were then used to identify a developmental stage- and cell type-specific core regulatory circuit super-enhancer (CRC-SE) upstream of the Vsx2 gene, which is required for bipolar neuron expression. Deletion of the Vsx2 CRC-SE in mice led to the loss of bipolar neurons in the retina.

neuroscience↗