Data transferability from model organisms to human beings: insights from the functional genomics of the flightless region of Drosophila. - Maleszka, R.; de Couet, H.G.; Miklos, G.L.G. (Proceedings of the National Academy of Sciences of the United States of America, 1998)
At what biological levels are data from single-celled organisms akin to a Rosetta stone for multicellular ones? To examine this question, we characterized a saturation-mutagenized 67-kb region of the Drosophila genome by gene deletions, transgenic rescues, phenotypic dissections, genomic and cDNA sequencing, bio-informatic analysis, reverse transcription-PCR studies, and evolutionary comparisons. Data analysis using cDNA/genomic DNA alignments and bio-informatic algorithms revealed 12 different predicted proteins, most of which are absent from bacterial databases, half of which are absent from Saccharomyces cerevisiae, and nearly all of which have relatives in Caenorhabditis elegans and Homo sapiens. Gene order is not evolutionarily conserved; the closest relatives of these genes are scattered throughout the yeast, nematode, and human genomes. Most gene expression is pleiotropic, and deletion studies reveal that a morphological phenotype is seldom observed when these genes are removed from the genome. These data pinpoint some general bottlenecks in functional genomics, and they reveal the acute emerging difficulties with data transferability above the levels of genes and proteins, especially with complex human phenotypes. At these higher levels the Rosetta stone analogy has almost no applicability. However, newer transgenic technologies in Drosophila and Mus, combined with coherency pattern analyses of gene networks, and synthetic neural modeling, offer insights into organismal function. We conclude that industrially scaled robogenomics in model organisms will have great impact if it can be realistically linked to epigenetic analyses of human variation and to phenotypic analyses of human diseases in different genetic backgrounds.