Showing papers by "Janet Hoenicka published in 2017"

The Addiction-Related Protein ANKK1 is Differentially Expressed During the Cell Cycle in Neural Precursors

Abstract: TaqIA is a polymorphism associated with addictions and dopamine-related traits. It is located in the ankyrin repeat and kinase domain containing 1 gene (ANKK1) nearby the gene for the dopamine D2 receptor (D2R). Since ANKK1 function is unknown, TaqIA-associated traits have been explained only by differences in D2R. Here we report ANKK1 studies in mouse and human brain using quantitative real-time PCR, Western blot, immunohistochemistry, and flow cytometry. ANKK1 mRNA and protein isoforms vary along neurodevelopment in the human and mouse brain. In mouse adult brain ANKK1 is located in astrocytes, nuclei of postmitotic neurons and neural precursors from neurogenic niches. In both embryos and adults, nuclei of neural precursors show significant variation of ANKK1 intensity. We demonstrate a correlation between ANKK1 and the cell cycle. Cell synchronization experiments showed a significant increment of ANKK1-kinase in mitotic cells while ANKK1-kinase overexpression affects G1 and M phase that were found to be modulated by ANKK1 alleles and apomorphine treatment. Furthermore, during embryonic neurogenesis ANKK1 was expressed in slow-dividing neuroblasts and rapidly dividing precursors which are mitotic cells. These results suggest a role of ANKK1 during the cell cycle in neural precursors thus providing biological support to brain structure involvement in the TaqIA-associated phenotypes.

Motion estimation of subcellular structures from fluorescence microscopy images

Abstract: We present an automatic image processing framework to study moving intracellular structures from live cell fluorescence microscopy. The system includes the identification of static and dynamic structures from time-lapse images using data clustering as well as the identification of the trajectory of moving objects with a probabilistic tracking algorithm. The method has been successfully applied to study mitochondrial movement in neurons. The approach provides excellent performance under different experimental conditions and is robust to common sources of noise including experimental, molecular and biological fluctuations.