Tuesday, October 15, 2013

How can non-coding mutations affect disease states?

Recent advances have made it easier to sequence the genome of an individual. With this information in hand it now becomes important to be able to use this information to make predictions about the effect that genetic mutations have on the chance that an individual will be affected by a disease. If a mutation occurs in a gene it is fairly straight forward to predict the effect that this alteration will have on the function of the protein encoded by the gene and subsequently the propensity to acquire a disease. However, genome wide studies that investigate the association of mutations with disease reveals that only 12% of the disease associated mutations occur in coding regions of the DNA. It, therefore, stands to reason that the remainder of these mutations will occur in regulatory elements, regions of DNA that bind transcription factors in order to control when and how strongly a gene is expressed. However, the mechanisms for genetic variation to affect regulation of transcription are incompletely understood.

Cartoon representation of the molecular structure of
transcription factor PU.1 residues 171-259 interacting with
a strand of DNA

by Jawahar Swaminathan





With this in mind scientists at the University of California-San Diego sought to characterize transcription factor binding across a genome. The results of their study are presented in the recent Nature article entitled: 'Effect of natural genetic variation on enhancer selection and function.' They used macrophage cells from two different mice strains and compared about 4 million DNA sequence differences. They chose the DNA sequences to analyze based on their binding to transcription factors using a technique called ChIP-Seq. This approach allows you to isolate the chromatin (DNA) which is bound to a protein by immune-precipitating the protein then perform sequencing of the DNA that is bound by the protein and therefore co-precipitates with the protein. Based on this they believe they have genomic data for lineage-determining transcription factor binding sites that can be used to study disease-associated variations in the DNA sequence.

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