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Review
. 2017:1042:489-526.
doi: 10.1007/978-981-10-6955-0_21.

Fragility Extraordinaire: Unsolved Mysteries of Chromosome Fragile Sites

Affiliations
Review

Fragility Extraordinaire: Unsolved Mysteries of Chromosome Fragile Sites

Wenyi Feng et al. Adv Exp Med Biol. 2017.

Abstract

Chromosome fragile sites are a fascinating cytogenetic phenomenon now widely implicated in a slew of human diseases ranging from neurological disorders to cancer. Yet, the paths leading to these revelations were far from direct, and the number of fragile sites that have been molecularly cloned with known disease-associated genes remains modest. Moreover, as more fragile sites were being discovered, research interests in some of the earliest discovered fragile sites ebbed away, leaving a number of unsolved mysteries in chromosome biology. In this review we attempt to recount some of the early discoveries of fragile sites and highlight those phenomena that have eluded intense scrutiny but remain extremely relevant in our understanding of the mechanisms of chromosome fragility. We then survey the literature for disease association for a comprehensive list of fragile sites. We also review recent studies addressing the underlying cause of chromosome fragility while highlighting some ongoing debates. We report an observed enrichment for R-loop forming sequences in fragile site-associated genes than genomic average. Finally, we will leave the reader with some lingering questions to provoke discussion and inspire further scientific inquiries.

Keywords: Aphidicolin; Cancer; Chromosome fragility; Common and rare fragile sites; DNA double-strand breaks; DNA replication stress; Folate stress; Neurological disorders; R-loops.

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Figures

Fig. 21.1
Fig. 21.1
Comparison between RFS expression frequencies induced by folate stress and by aphidicolin (APH). Experimental values obtained from Kähkönen et al. (1989) and Mrasek et al. (2010), respectively
Fig. 21.2
Fig. 21.2
Probability plots showing the distribution of 1973 genes within RFSs by the number of RLFSs per gene (A) or by the number of RLFSs per kb for a given gene (B). The top 1% of genes in each plot are as shown

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