Although the APOBEC3 family of single-stranded DNA cytosine deaminases is well-known for its antiviral factors, these enzymes are rapidly gaining attention as prominent sources of mutation in cancer. APOBEC3′s signature single-base substitutions, C-to-T and C-to-G in TCA and TCT motifs, are evident in over 70% of human malignancies and dominate the mutational landscape of numerous individual tumors. Recent murine studies have established cause-and-effect relationships, with both human APOBEC3A and APOBEC3B proving capable of promoting tumor formation in vivo. Here, we investigate the molecular mechanism of APOBEC3A-driven tumor development using the murine Fah liver complementation and regeneration system. First, we show that APOBEC3A alone is capable of driving tumor development (without Tp53 knockdown as utilized in prior studies). Second, we show that the catalytic glutamic acid residue of APOBEC3A (E72) is required for tumor formation. Third, we show that an APOBEC3A separation-of-function mutant with compromised DNA deamination activity and wildtype RNA-editing activity is defective in promoting tumor formation. Collectively, these results demonstrate that APOBEC3A is a “master driver” that fuels tumor formation through a DNA deamination-dependent mechanism.

Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases.

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We find the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Breast cancer is one of the most prevalent cancers in women worldwide. Through the regulation of many coding and non-coding target genes, oestrogen (E₂ or 17β-oestradiol) and its nuclear receptor ERα play important roles in breast cancer development and progression. Despite the astounding advances in our understanding of oestrogen-regulated coding genes over the past decades, our knowledge on oestrogen-regulated non-coding targets has just begun to expand. Here we leverage epigenomic approaches to systematically analyse oestrogen-regulated long non-coding RNAs (lncRNAs). Similar to the coding targets of ERα, the transcription of oestrogen-regulated lncRNAs correlates with the activation status of ERα enhancers, measured by eRNA production, chromatin accessibility, and the occupancy of the enhancer regulatory components including P300, MED1, and ARID1B. Our 3D chromatin architecture analyses suggest that lncRNAs and their neighbouring E₂-resonsive coding genes, exemplified by LINC00160 and RUNX1, might be regulated as a 3D structural unit resulted from enhancer-promoter interactions. Finally, we evaluated the expression levels of LINC00160 and RUNX1 in various types of breast cancer and found that their expression positively correlated with the survival rate in ER+ breast cancer patients, implying that the oestrogen-regulated LINC00160 and its neighbouring RUNX1 might represent potential biomarkers for ER+ breast cancers.