Biomedical subjects

Jing Ou

Publications and source records attributed to Jing Ou.

4 recordsLinked to original sources

PubMed2005

Maternal embryonic leucine zipper kinase (MELK) regulates multipotent neural progenitor proliferation.

Maternal embryonic leucine zipper kinase (MELK) was previously identified in a screen for genes enriched in neural progenitors. Here, we demonstrate expression of MELK by progenitors in developing and adult brain and that MELK serves as a marker for self-renewing multipotent neural progenitors (MNPs) in cultures derived from the developing forebrain and in transgenic mice. Overexpression of MELK enhances (whereas knockdown diminishes) the ability to generate neurospheres from MNPs, indicating a function in self-renewal. MELK down-regulation disrupts the production of neurogenic MNP from glial fibrillary acidic protein (GFAP)-positive progenitors in vitro. MELK expression in MNP is cell cycle regulated and inhibition of MELK expression down-regulates the expression of B-myb, which is shown to also mediate MNP proliferation. These findings indicate that MELK is necessary for proliferation of embryonic and postnatal MNP and suggest that it regulates the transition from GFAP-expressing progenitors to rapid amplifying progenitors in the postnatal brain.

Animals↗

PubMed2004

Mutations in a human ROBO gene disrupt hindbrain axon pathway crossing and morphogenesis.

The mechanisms controlling axon guidance are of fundamental importance in understanding brain development. Growing corticospinal and somatosensory axons cross the midline in the medulla to reach their targets and thus form the basis of contralateral motor control and sensory input. The motor and sensory projections appeared uncrossed in patients with horizontal gaze palsy with progressive scoliosis (HGPPS). In patients affected with HGPPS, we identified mutations in the ROBO3 gene, which shares homology with roundabout genes important in axon guidance in developing Drosophila, zebrafish, and mouse. Like its murine homolog Rig1/Robo3, but unlike other Robo proteins, ROBO3 is required for hindbrain axon midline crossing.

Adult↗

PubMed2003

Neural progenitor genes. Germinal zone expression and analysis of genetic overlap in stem cell populations.

The identification of the genes regulating neural progenitor cell (NPC) functions is of great importance to developmental neuroscience and neural repair. Previously, we combined genetic subtraction and microarray analysis to identify genes enriched in neural progenitor cultures. Here, we apply a strategy to further stratify the neural progenitor genes. In situ hybridization demonstrates expression in the central nervous system germinal zones of 54 clones so identified, making them highly relevant for study in brain and neural progenitor development. Using microarray analysis we find 73 genes enriched in three neural stem cell (NSC)-containing populations generated under different conditions. We use the custom microarray to identify 38 "stemness" genes, with enriched expression in the three NSC conditions and present in both embryonic stem cells and hematopoietic stem cells. However, comparison of expression profiles from these stem cell populations indicates that while there is shared gene expression, the amount of genetic overlap is no more than what would be expected by chance, indicating that different stem cells have largely different gene expression patterns. Taken together, these studies identify many genes not previously associated with neural progenitor cell biology and also provide a rational scheme for stratification of microarray data for functional analysis.

Animals↗

PubMed2002

Grouped-neural network modeling for model predictive control.

A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.

Algorithms↗