Mathematical Methods to Study the Relative Position of Chromosomes During Interphase in Human Cells
Presenter
September 20, 2007
Abstract
During interphase chromosomes are confined to sub-nuclear regions called chromosome territories. The position of these territories has been associated with a number of biological processes such as cell differentiation and transcription. Furthermore the relative position of chromosomes is believed to have a very important role in the formation of chromosome aberrations in cancer and other human diseases.
Ionizing radiation helps approach the problem of the relative position of chromosomes. When ionizing radiation tracks (gamma rays, X-rays, high energy alpha particles, etc) cross the cell nucleus they release enough energy to disrupt the molecular structure of the DNA (directly or indirectly) and induce DNA double stranded-breaks (DSBs). When DSB free ends are rejoined with free ends different from their original partners, chromosome aberrations are introduced. If the two misrejoined free ends are on different chromosomes, the chromosome aberration is called an interchange, and the number of interchanges observed for each chromosome pair can help indicate chromosome positioning in the nucleus.
In this presentation I will show our current mathematical methods to interrogate mFISH or SKY data for chromosome clustering (i.e. for deviations from randomness in the relative positions of chromosomes). Our approach is based on the hypothesis that chromosomes that are in close proximity form radiation induced chromosome aberrations more often than those that are far apart (known as the proximity effect hypothesis). When applying these methods to human lymphocytes we find two sets of chromosomes that are on average closer to each other than what randomness would predict these are: {1,16,17,19,22}and {13,14,15,21,22} (Cornforth et al. 2002, Arsuaga et al. 2004, Vives et al. 2005).
We are currently characterizing features of nuclear organization that different types of radiations can detect. We are also extending our studies to human fibroblasts for which we are developing data mining methods.