Loss of neuroprotective survival signal in mice lacking insulin receptor gene in rod photoreceptor cells

Insulin receptor (IR) signaling provides a trophic signal for transformed retinal neurons in culture, but the role of IR activity in vivo is unknown. We previously reported that light causes increased tyrosine phosphorylation of the IR in vivo, which leads to the downstream activation of the phosphoinositide 3-kinase and Akt pathway in rod photoreceptor cells. The functional role of IR in rod photoreceptor cells is not known. We observed that light stress induced tyrosine phosphorylation of the IR in rod photoreceptor cells, and we hypothesized that IR activation is neuroprotective. To determine whether IR has a neuroprotective role on rod photoreceptor cells, we used the Cre/lox system to specifically inactivate the IR gene in rod photoreceptors. Rod-specific IR knock-out mice have reduced the phosphoinositide 3-kinase and Akt survival signal in rod photoreceptors. The resultant mice exhibited no detectable phenotype when they were raised in dim cyclic light. However, reduced IR expression in rod photoreceptors significantly decreased retinal function and caused the loss of photoreceptors in mice exposed to bright light stress. These results indicate that reduced expression of IR in rod photoreceptor cells increases their susceptibility to light-induced photoreceptor degeneration. These data suggest that the IR pathway is important for photoreceptor survival and that activation of the IR may be an essential element of photoreceptor neuroprotection.     

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

Background  

Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data.