Many ligands that affect nervous system development are members of gene families that function together to coordinate the assembly of complex neural circuits. cpg15/neuritin encodes an extracellular ligand that promotes neurite growth, neuronal survival, and synaptic maturation. Here we identify cpg15-2 as the only paralogue of cpg15 in the mouse and human genome. Both genes are expressed predominantly in the nervous system, where their expression is regulated by activity. cpg15-2 expression increases by more than twofold in response to kainate-induced seizures and nearly fourfold in the visual cortex in response to 24 hours of light exposure following dark adaptation. cpg15 and cpg15-2 diverge in their spatial and temporal expression profiles. cpg15-2 mRNA is most abundant in the retina and the olfactory bulb, as opposed to the cerebral cortex and the hippocampus for cpg15. In the retina, they differ in their cell-type specificity. cpg15 is expressed in retinal ganglion cells, whereas cpg15-2 is predominantly in bipolar cells. Developmentally, onset of cpg15-2 expression is delayed compared with cpg15 expression. CPG15-2 is glycosylphosphatidylinositol (GPI) anchored to the cell membrane and, like CPG15, can be released in a soluble-secreted form, but with lower efficiency. CPG15 and CPG15-2 were found to form homodimers and heterodimers with each other. In hippocampal explants and dissociated cultures, CPG15 and CPG15-2 promote neurite growth and neuronal survival with similar efficacy. Our findings suggest that CPG15 and CPG15-2 perform similar cellular functions but may play distinct roles in vivo through their cell-type- and tissue-specific transcriptional regulation.
Pubmed ID: 18265009 RIS Download
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View all literature mentionsWeb application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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