(Taken from the Candidate's Abstract) At-Risk DNA Motifs (ARMS), which include repetitive elements such as Alu sequences, homonucleotide runs and triplet repeats, are potentially unstable segments of the human genome. ARMS are a factor in genetic susceptibility to disease, requiring particular combinations of genetic backgrounds and environmental triggers to express a disease phenotype. While some of the mechanisms are understood, it is not clear under what circumstances repetitive DNA elements mediate pathological mutagenesis. Although a high burden of these sequences is generally tolerated in humans, they can have an enormous impact on health by contributing to diseases that have devastating effects on afflicted individuals. For example, Alus have been linked to numerous diseases including Fanconi anemia, alphazerothalassemia, leukemia, hypertension, neurofibromatosis, breast, and colon cancers. Trinucleotide repeat expansions have been linked with Kennedy's Disease, Huntington's Disease, myotonic muscular dystrophy, and Friedreich ataxia. The long term objective of this proposal is to gain insight into the genetic factors that mitigate gene rearrangement in hopes of predicting when the presence of a repetitive element truly constitutes a threat to the health of an individual. The hypothesis is that the characterization of ARMS according to all possible attributes (i.e. size of repeats, separation distances between repeats, orientation, sequence similarity between repeats, nucleotide base constitution and proximity and/or containment of mutagenic and/or toxicological agent targets, DNA processive or other enzymatic target sites) can reveal largely excluded situations that can be viewed as unstable. It is also postulated that a multidimensional database of repetitive sequences characterized according to the aforementioned attributes can be used to predict repetitive elements that are most prone to mutation, ARMS, while increasing our understanding of the interactions between these genetic elements and their environment. The approach is to use a combination of computational biology and molecular genomic analysis to locate and analyze ARMS.
The specific aims of this proposal are to: 1) characterize available data according to the conceivable relevant attributes of size, distance, orientation, degree of homology, base constitution and containment of known target sequences. 2) To test the hypothesis by computationally identifying loci that have already known to contain ARMS linked to a mutation resulting in disease, and then to identify specific genes that may be at-risk for mutation and experimentally testing them using molecular biological approaches. 3) To set up an interactive on-line database and program server so that the scientific community can use the information and apply it to drive experimental research.
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