Pathways to cognitive deficits in Down syndrome.

Major efforts in Down syndrome (DS) research have been directed at the identification and functional characterization of genes encoded by human chromosome 21 (HSA21). In parallel with this, tissue samples and cell lines derived from individuals with DS have been examined for abnormalities in gene expression and cellular morphology, and mouse models of DS have been characterized for abnormalities at the molecular, cellular, electrophysiological, and behavioral level. One goal of such investigations has been the identification of effective targets for pharmacotherapies that can prevent or correct the abnormalities and, by extension to human clinical trials, prevent or lessen aspects of the cognitive deficits seen in people with DS. Because it is caused by an extra copy of an entire chromosome, DS has been considered by some as too complicated a genetic perturbation to be amenable to postnatal pharmacological interventions. However, recent data from experiments with one mouse model, the Ts65Dn, have clearly demonstrated that several pharmacological interventions can indeed rescue DS-relevant learning and memory deficits. Extension of mouse data to successful human clinical trials will be aided by understanding the molecular basis of successful drug treatments, that is, how increased expression of HSA21 genes perturbs molecular mechanisms that are targeted and rescued by specific drugs. Here, we review information on HSA21 genes, their expression and their likely contributions to the DS phenotype. We then describe results of a bioinformatics effort that integrates information on genes known to cause intellectual disability when mutated, the pathways in which these genes function, and how these pathways are impacted by HSA21 encoded proteins. This pathway approach to the molecular basis of ID in DS aids in understanding why some drug therapies have been successful in the Ts65Dn and in predicting whether these same drugs are likely to be successful in treating ID in DS. These data can be used to design new experiments and interpret information for prediction of additional targets for effective drug treatments.