Ashlyn Thorn and Alex Wagner will present their research on 19th April 2012
Ashlyn Thorn’s research abstract:
An Expanded Model of Synaptic Homeostasis: Novel Roles for Csk, Src, and a FGFR
Synaptic homeostasis is the process by which synapses regulate their function to maintain normal neurotransmission and postsynaptic responses. Perturbation of this process results in aberrant neuronal signaling which has led to the idea that synaptic homeostasis plays a role in neurological disorders like epilepsy, migraine, schizophrenia, ataxia and myasthenia gravis. We study the genetic mechanisms that underlie synaptic homeostasis using the larval neuromuscular junction of D. melanogaster. Previous work with this system has revealed a presynaptic pathway composed of the Eph receptor, the Rho-type GEF Ephexin, the GTPase Cdc42, and voltage-gated Cav2 calcium channels. Recently, we have undertaken and RNAi-based screen to identify additional members of this pathway. A number of candidates were identified. Among these were several factors known to interact with the Eph receptor and ephexin. Verification of these candidates with mutant alleles has revealed C-terminal Src kinase (Csk) and a fibroblast growth factor receptor (FGFR), Heartless (Htl), as factors required for synaptic homeostasis. Csk functions in many cellular contexts to phosphorylate and down-regulate the activity of Src family kinases. Src, in turn, is known to phosphorylate ephexin and regulate its preference for different GTPase substrates such as RhoA, Rac, or Cdc42. Htl has also been shown to phosphorylate ephexin and is known to physically interact with the Eph receptor. Consistent with these observations, preliminary data suggests that Drosophila src homologs may also be involved in synaptic homeostasis and that Csk is genetically interacting with established members of the synaptic homeostasis pathway. A model of synaptic homeostasis updated to include Csk, Src, and Htl will be presented.
Alex Wagner’s research abstract:
Machine Learning Techniques to Identify Genetic Factors in Retinal Disease
Diseases of the retina are complex disorders caused by numerous genetic factors. Identifying the genetic factors contributing to the disease phenotype for a patient can lead to a greater understanding of disease progression, heritability, and potential treatments. Our goal is to use machine learning techniques to identify features that best discern a sub-set of known retina disease-causing genes from all expressed genes using cone-rod homeobox binding site, microarray, and RNA-seq data from various eye tissues. If successful, we can apply this model using the most informative feature set to prioritize novel disease gene candidates identified in exome sequencing experiments.