mouse chromosome 16 from Sod1 through Mx (and again presumably to the telomere). While it has not yet been studied so thoroughly as Ts65Dn, there are phenotypic differences between the two mice. In contrast to Ts65Dn, Ts1Cje shows hypoactivity, no loss of cholinergic neurons, and no deficits in the visible platform part of the water maze tests (which tests only memory and not the ability to make spatial correlations) . Table Table3b3b lists 27 genes that are expected to be trisomic in the Ts65Dn but only disomic in the Ts1Cje, based on the genetic map . It is tempting to conclude that these genes must account for the phenotypic differences, but it must be kept in mind that the two mouse strains have been produced on different genetic backgrounds, which may have phenotypic consequences.
Segmental trisomies for the regions of chromosome 21 homologous with mouse chromosomes 17 and 10 do not exist. If Mx is the most telomeric gene on mouse chromosome 16 and Pdxk is the most centromeric on mouse chromosome 10, there are 33 genes within the approximately 2.2 Mb of the mouse chromosome 17 region (Table (Table4)4) and 50 genes within the approximately 2.9 Mb of the mouse chromosome 10 region. Adding the maximum of 32 genes not trisomic in the Ts65Dn, half of the chromosome 21 homologous genes are not trisomic in Ts65Dn. The phenotypic consequences of these genes must be assessed in some fashion, because the Ts65Dn lacks some features of DS. Constructing single-gene transgenic mice expressing each of these and then combining each with the Ts65Dn by breeding would be laborious and probably of limited success. An alternative is to generate additional segmental trisomies using the Cre-lox system .
Analysis of the complete sequence of chromosome 21 has provided the first look at all candidate DS genes. The next steps require verifying and refining the predicted, incomplete gene models, defining new models as necessary, and isolating complete cDNAs for each gene. With complete coding sequences, protein sequences can be examined for motifs, domains, and biochemical characteristics that may suggest function. The most challenging problem will then be determining the functions of these genes and the other 'known' genes. While it is tempting to focus on genes whose protein characteristics suggest a hypothesis for relevance to some aspect of DS, the more than 100 genes distributed throughout the chromosome that have no functional association are too large a dataset to ignore. For these and other genes on 21q, detailed expression analysis may be informative. Demonstration that a gene shows increased expression in the trisomic state by northern blot or RT-PCR analysis, followed by RNA tissue in situ hybridization to define specific cell types, brain regions and developmental stages of expression, may help in selecting genes of greater or lesser interest.
The most direct assessment of function will require mutation or overexpression of individual genes or sets of genes. For these experiments, the 'complete' protein databases for S. cerevisiae, C. elegans and Drosophila will provide homologous genes that can be analyzed in more tractable systems. The increasing complexity of the zebrafish EST database will add another model organism system of increasing utility. Issues remain with all model organisms, however, of verifying correct gene structures, identifying orthologous genes versus merely homologous genes, and interpreting mutation and knockout data in one system versus overexpression in another. The ultimate model organism, of course, will remain the mouse. Multiple genes can be 'added' to the Ts65Dn using transgenics carrying bacterial chromosomes (BACs), to look for enhanced DS-relevant phenotypes. The human sequence will be useful here in ensuring that clones are extensive enough to contain appropriate regulatory regions. Single-gene knockouts can also be 'subtracted' from the Ts65Dn mouse model, to search for amelioration of phenotype. With good biological intuition and luck, it may not be necessary to understand all of the genes within 