FGF-2 Deficiency Does Not Influence FGF Ligand and Receptor Expression during Development of the Nigrostriatal System

Secreted proteins of the fibroblast growth factor (FGF) family play important roles during development of various organ systems. A detailed knowledge of their temporal and spatial expression profiles, especially of closely related FGF family members, are essential to further identification of specific functions in distinct tissues. In the central nervous system dopaminergic neurons of the substantia nigra and their axonal projections into the striatum progressively degenerate in Parkinson's disease. In contrast, FGF-2 deficient mice display increased numbers of dopaminergic neurons. In this study, we determined the expression profiles of all 22 FGF-ligands and 10 FGF-receptor isoforms, in order to clarify, if FGF-2 deficiency leads to compensatory up-regulation of other FGFs in the nigrostriatal system. Three tissues, ventral mesencephalon (VM), striatum (STR) and as reference tissue spinal cord (SC) of wild-type and FGF-2 deficient mice at four developmental stages E14.5, P0, P28, and adult were comparatively analyzed by quantitative RT-PCR. As no differences between the genotypes were observed, a compensatory up-regulation can be excluded. Moreover, this analysis revealed that the majority of FGF-ligands (18/22) and FGF-receptors (9/10) are expressed during normal development of the nigrostriatal system and identified dynamic changes for some family members. By comparing relative expression level changes to SC reference tissue, general alterations in all 3 tissues, such as increased expression of FGF-1, -2, -22, FgfR-2c, -3c and decreased expression of FGF-13 during postnatal development were identified. Further, specific changes affecting only one tissue, such as increased FGF-16 (STR) or decreased FGF-17 (VM) expression, or two tissues, such as decreased expression of FGF-8 (VM, STR) and FGF-15 (SC, VM) were found. Moreover, 3 developmentally down-regulated FGFs (FGF-8b, FGF-15, FGF-17a) were functionally characterized by plasmid-based over-expression in dissociated E11.5 VM cell cultures, however, such a continuous exposure had no influence on the yield of dopaminergic neurons in vitro

A boolean model of the gene regulatory network underlying mammalian cortical area development

The cerebral cortex is divided into many functionally distinct areas. The emergence of these areas during neural development is dependent on the expression patterns of several genes. Along the anterior-posterior axis, gradients of Fgf8, Emx2, Pax6, Coup-tfi, and Sp8 play a particularly strong role in specifying areal identity. However, our understanding of the regulatory interactions between these genes that lead to their confinement to particular spatial patterns is currently qualitative and incomplete. We therefore used a computational model of the interactions between these five genes to determine which interactions, and combinations of interactions, occur in networks that reproduce the anterior-posterior expression patterns observed experimentally. The model treats expression levels as Boolean, reflecting the qualitative nature of the expression data currently available. We simulated gene expression patterns created by all 1.68 x 10(7) possible networks containing the five genes of interest. We found that only 0.1% of these networks were able to reproduce the experimentally observed expression patterns. These networks all lacked certain interactions and combinations of interactions including auto-regulation and inductive loops. Many higher order combinations of interactions also never appeared in networks that satisfied our criteria for good performance. While there was remarkable diversity in the structure of the networks that perform well, an analysis of the probability of each interaction gave an indication of which interactions are most likely to be present in the gene network regulating cortical area development. We found that in general, repressive interactions are much more likely than inductive ones, but that mutually repressive loops are not critical for correct network functioning. Overall, our model illuminates the design principles of the gene network regulating cortical area development, and makes novel predictions that can be tested experimentally