Genomics, morphogenesis and biophysics: triangulation of Purkinje cell development.

The cerebellar Purkinje cells (P-cells) comprise an organelle that is suitable for combined analysis by morphology and genomics, using biophysical tools. In some unknown way, genomic information specifies the development of P-cells. One of us (AJP) has previously proposed that fractal processes associated with DNA are in a causal relation to the fractal properties of organelles such as P-cells (FractoGene, 2002, patent pending). This fractal postulate predicts that the dendritic arborization of P-cells will be less complex in lower order vertebrates. The prediction can be tested by systematic comparative neuroanatomy of the P-cell in species for which genome sequences permit inter-species comparison. The Fugu rubripes (Fugu), Danio rerio (Danio) and other species are lower order vertebrates for which genome sequences are available and tests could be conducted. Consistent with the fractal prediction, P-cell dendritic arbor is primitive in Fugu, being much less complex than in Mus musculus and in Homo sapiens. Genomic analysis readily identified PEP19/Pcp4, Calbindin-D28k, and GAD67 genes in Fugu and in Danio that are closely associated with P-cells in Canis familiaris, Rattus norvegicus, Mus musculus and Homo sapiens. Gene L7/Pcp2 exhibits strongest association with P-cells in higher vertebrates. L7/Pcp2 shows strong protein residue homology with genes greater than 600 residues and including 2-3 GoLoco domains, designated as having G protein signaling modulator function (AGS3-like proteins). Fugu has a short gene with a single GoLoco domain, but it has greatest homology with the AGS3-like proteins. No similar short gene is present in Danio or in Xenopus. Classical L7/Pcp2 is only detected in higher vertebrates, suggesting that it may be a marker of more recent evolutionary development of cerebellar P-cells. We expect that a new generation of data mining tools will be required to support recursive fractal geometrical, combinatorial, and neural network models of the genomic basis of morphogenesis.