3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations

Pengze Wu,Yuezhou Yu,Yifang Liu,Cheng Li,Daiwei Tang,Ping Zhu,Tingting Li,Lumeng Jia,Ruifeng Li,Qing Chen

doi:10.1038/s41467-017-01793-w

Pengze Wu, Yuezhou Yu + Show 8 more

Open Access

https://doi.org/10.1038/s41467-017-01793-w

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Abstract

The Hi-C method is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. Here, we integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect CNVs. Also, combining Hi-C and WGS data can improve the detection of translocations. We find that CNV breakpoints significantly overlap with topologically associating domain (TAD) boundaries. Compared to normal B cells, the numbers of TADs increases by 25% in MM, the average size of TADs is smaller, and about 20% of genomic regions switch their chromatin A/B compartment types. In summary, we report a 3D genome interaction map of aneuploid MM cells and reveal the relationship among CNVs, translocations, 3D genome reorganization, and gene expression regulation.

Highlights

The karyotypes of the two MM cell lines were further confirmed by copy number variations (CNVs) analysis of whole-genome sequencing (WGS) data sets (Supplementary Fig. 1e)
Besides CNVs, we found that genomic structure alterations such as translocations can be reflected by Hi-C interaction matrices
We investigated the 3D genome of aneuploid cancer and performed integrated analyses of Hi-C, WGS, and RNA-seq experiments in two MM cell lines

Summary

Introduction

We integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect. CNVs. combining Hi-C and WGS data can improve the detection of translocations. We find that CNV breakpoints significantly overlap with topologically associating domain (TAD). Compared to normal B cells, the numbers of TADs increases by 25% in MM, the average size of TADs is smaller, and about 20% of genomic regions switch their chromatin. MM cells and reveal the relationship among CNVs, translocations, 3D genome reorganization, and gene expression regulation

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