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Molecular Biological and Molecular Genetic studies of the Mechanisms Underlying Neurodegeneration in Alzheimer's Disease

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Epidemiological studies of AD have revealed that the two strongest risk factors are increasing age and a positive family history. While a positive family history does not necessarily imply inherited factors, molecular genetic tools have been applied to familial aggregates of AD during the past 30 years. These tools, beginning first with genetic linkage studies in early onset autosomal dominant pedigrees have revealed that missense mutations in the amyloid precursor protein (APP), presenilin-1 (PS1), and presenilin-2 (PS2) are capable of causing this disease. Biochemical analyses of the effects of these mutations tells a convincing story that misprocessing of APP , and the overproduction of aggregation-prone Abeta peptide is an important causative factor at least in this form of AD. This knowledge has in turn spurred on efforts to generate disease-modifying therapies for AD based upon blocking the production/effect of Abeta.

Subsequent studies using case:control designs to test associations with candidate genes have led to the discovery that the E4 allele of Apolipoprotein E (APOE ε4) is strongly associated with late onset AD (and is in fact the strongest genetic risk factor known to date). Similar studies with multiple other candidate genes have indicted a long list of these genes, several of which (SORL1, SORCS1 , ACE1 amongst them) appear to be reasonably robust, having been replicated in several independent series. More recently, hypothesis-free genome-wide association studies (GWAS) have been applied, and have reported confirmed associations with PICALM , clusterin (CLU/APOEJ) and complement receptor 1 (CR1). Some useful questions can addressed with these new AD genes. First of all, do they act in Abeta-generating pathways? Are they part of the downstream effector pathways by which Abeta acts? Are they in pathways that cause Alzheimer’s disease completely independent of Abeta? Answers to these questions will be identified once the disease-causing mutations are known, and can be assessed in cellular and animal models. The identification of the remaining genes that underlie the unaccounted-for genetic risk poses a more difficult problem.

This talk is part of the Biophysical Colloquia series.

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