Gene promoters show chromosome-specificity and reveal chromosome territories in humans

Background: Gene promoters have guided evolution processes for millions of years. It seems that they were the main engine responsible for the integration of different mutations favorable for the environmental conditions. In cooperation with different transcription factors and other biochemical components, these regulatory regions dictate the synthesis frequency of RNA molecules. Predominantly in the last decade, it has become clear that nuclear organization impacts upon gene regulation. To fully understand the connections between Homo sapiens chromosomes and their gene promoters, we analyzed 1200 promoter sequences using our Kappa Index of Coincidence method. Results: In order to measure the structural similarity of gene promoters, we used two-dimensional image-based patterns obtained through Kappa Index of Coincidence (Kappa IC) and (C+G)% values. The center of weight of each promoter pattern indicated a structure similarity between promoters of each chromosome. Furthermore, the proximity of chromosomes seems to be in accordance to the structural similarity of their gene promoters. The arrangement of chromosomes according to Kappa IC values of promoters, shows a striking symmetry between the chromosome length and the structure of promoters located on them. High Kappa IC and (C+G)% values of gene promoters were also directly associated with the most frequent genetic diseases. Taking into consideration these observations, a general hypothesis for the evolutionary dynamics of the genome has been proposed. In this hypothesis, heterochromatin and euchromatin domains exchange DNA sequences according to a difference in the rate of Slipped Strand Mispairing and point mutations. Conclusions: In this paper we showed that gene promoters appear to be specific to each chromosome. Furthermore, the proximity between chromosomes seems to be in accordance to the structural similarity of their gene promoters. Our findings are based on comprehensive data from Transcriptional Regulatory Element Database and a new computer model whose core is using Kappa index of coincidence.