Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration

Abstract

3D genome organization regulates gene expression in different physiological and pathological contexts. Characterization of chromatin structure at different scales has provided information about how the genome organizes in the nuclear space, from chromosome territories, compartments of euchromatin and heterochromatin, topologically associated domains to punctual chromatin loops between genomic regulatory elements and gene promoters. In recent years, chromosome conformation capture technologies have also been used to characterize structural variations (SVs) de novo in pathological conditions. The study of SVs in cancer, has brought information about transcriptional misregulation that relates directly to the incidence and prognosis of the disease. For example, gene fusions have been discovered arising from chromosomal translocations that upregulate oncogenes expression, and other types of SVs have been described that alter large genomic regions encompassing many genes. However, studying SVs in 2D cannot capture all their regulatory implications in the genome. Recently, several bioinformatic tools have been developed to identify and classify SVs from chromosome conformation capture data and clarify how they impact chromatin structure in 3D, resulting in transcriptional misregulation. Here, we review recent literature concerning bioinformatic tools to characterize SVs from chromosome conformation capture technologies and exemplify their vast potential to rebuild the 3D landscape of genomes in cancer. The study of SVs from the 3D perspective can produce essential information about drivers, molecular targets, and disease evolution.

Keywords: chromatin; chromatin architecture; chromosome conformation capture (3C); structural variation (SV); topologically associated domains.

FIGURE 1

Hi-C is a powerful tool for characterizing structural variants (SVs) in altered genomes. Upper panel: Matrix derived from Hi-C experiments from rearranged genomes shows atypical interactome patterns that can represent inter-chromosomal translocations or intra-chromosomal SV. These alterations can be observed in the matrices and also can be identified and annotated by various bioinformatic tools. Middle panel: Hi-C data can identify complex structural variants like chromothripsis and permit the reconstruction of the region affected by an SV using the interaction patterns. Lower panel: SV can result in the formation of new topologically associated domains and interaction loops between the altered regions. The consequences of these rearrangements could trigger gene fusions, ectopic interaction and enhancer hijacking events. There are bioinformatic tools developed to assess the tridimensional consequences of SV using Hi-C data.

FIGURE 2

Chromosome conformation capture techniques as tools for the identification of structural variants. (A) Hi-C experiments can identify translocations present in a low percentage of the cells. Translocation of the chromosome 12 and 21 is a frequent genome alteration found in B-cell acute lymphoblastic leukemia (B-ALL) patients and results in the fusion of ETV6-RUNX1 genes. Using a blood and bone marrow sample from a pediatric patient with B-ALL, a Hi-C experiment could identify the balanced translocation present in both samples and also allowed to visually assess the breakpoint of the SV. (B) 4C can identify genomic rearrangements in a very sensitive manner. A mixture of HSB-2 cell line harboring a known translocation t(1;7) and K562 cell line was analyzed by 4C using the region next to the breakpoint in chromosome 7 as anchor. The percentage of altered cells was diluted until 5% of the mixture presented the translocation. 4C data showed the presence of the SV even when only represented by a low percentage of the genomes in the mix.