Three-dimensional genome: developmental technologies and applications in precision medicine

Abstract

In the 20th century, our familiar structure of DNA was the double helix. Due to technical limitations, we do not have a good way to understand the finer structure of the genome, let alone its transcriptional regulation. Until the advent of 3C technologies, we were no longer blind to this one. Three-dimensional (3D) genomics is a new subject, which mainly studies the 3D structure and transcriptional regulation of eukaryotic genomes. Now, this field mainly has Hi-C series and CHIA-PET series technologies. Through 3D genomics, we can understand the basic structure of DNA, understand the growth and development of organisms and the occurrence of diseases, so as to promote human medical and health undertakings. The review introduces the main research techniques of 3D genomics and their characteristics, the latest development of 3D genome structure, the relationship between diseases and 3D genome structure, the applications of 3D genome in precision medicine, and the development of the 4D nucleome project.

Introduction

What is the nature of life? In 1944, Avery and Griffiths confirmed that DNA was the genetic material through the pneumococcal conversion experiment, which made new progress in the exploration of life. In 1953, the emergence of the concept of the DNA double helix structure set the stage for molecular biology. Scientists can interpret the genomic information brought about by DNA at the molecular level. With the help of the Human Genome Project [1] and the Human Genome Encyclopedia Program [2], scientists have been granted a blueprint for the human genome, defining the structure and function of each gene. However, in the course of two major planned studies, scientists found that DNA length was about 2 m in the nucleus, while the nucleus was about 8–20 μm in diameter. The narrow space does not constitute a harmonious relationship with the complex DNA function, which makes people confusion. How did DNA assemble in the nucleus? How exactly does DNA express itself in the nucleus? How to make sure the complete expression of biological traits? It depends on the problems are solved, which respectively are what is the three-dimensional (3D) structure of DNA and how DNA transcription to express. The development of 3D genome technologies, such as 3C technology [3], provides a satisfactory solution to the problems. By using the 3D genome techniques such as 3C technology, scientists can study the 3D structure of the genome and depict the molecular mechanism of DNA transcription, which is 3D genomics.

This review describes the main techniques of 3D genome and their advantages and disadvantages, the latest advances in DNA structure, the relationship between disease and 3D genome structure, the clinical application of 3D genome technologies, and the 4D nucleome project.

The main research techniques of 3D genomics

There are for two kinds of techniques in the study of 3D genome structure, one is microscopic imaging technology and the other is the chromatin conformation capture technology based on sequencing. Microscopic imaging techniques mainly include fluorescence in situ hybridization [4] and ChromEMT tomography by scanning electron microscopy [5]. The former can show the specific area of chromatin in the nucleus, and the latter can further analyze the 3D genome structure and folding of chromatin in situ. Although the characteristics of chromatin spatial structure can be observed in microscopic imaging technology, its flux and resolution are low, which does not contribute to 3D genome research. Chromatin conformation capture technology based on sequencing can solve the disadvantages of low flux and low resolution of microscopic imaging technology. The following was the development of chromatin conformation capture technologies. In 2002, 3C technology was designed to apply to the interaction of two known genomic loci at chromatin [3]. In 2006, 4C technology [6] based on 3C technology was invented and can be applied to the interaction of a known gene locus with multiple unknown gene loci. Based on the purpose of high-efficiency testing, 5C technology has been developed to apply to the interaction of multiple unknown gene loci with multiple known gene loci [7]. In 2009, Hi-C was invented to analyze genome-wide chromatin interactions using the characteristics that biotin can be bind to the viscous end of the enzyme-cut fragment [8]. Utilizing Hi-C technology, the formation principle of chromatin ring under the resolution of thousands of bases can be analyzed and detected [9]. Unfortunately, Hi-C does not recognize the interaction of redundant chromatin fragments, and it is cumbersome and expensive to operate. However, DLO Hi-C [10] solves the shortcoming. The DLO Hi-C passes two rounds of digestion, eliminating unnecessary fragments without biotin markers, improving efficiency, and being affordable for comfortable use in any laboratory. The Hi-C sequencing method handles thousands of cells at a time, and the level of variation obtained is also the average post-level of thousands of cells.

In recent years, the appearance of single-cell Hi-C has solved the problem of retaining the special characteristics of different cells, not only can analyze the interaction of chromatin in a single cell, but also can see the difference of chromatin interaction between different cells [11,12,13,14]. As a result, single-cell Hi-C has become a popular technology at the moment. Although the single-cell Hi-C reveals the heterogeneity of the chromatin structure, its resolution is not sufficient to see a clear TAD structure [12, 15, 16]. Chen et al. developed MERFISH technology, which can be used to identify thousands of RNA molecules in cells in high-throughput in situ [17]. Later, Wang et al. applied the technique to in situ imaging genomic DNA [18], and under the step of adding two-stage hybridization, MERFISH became a method that could trace the chromatin structure at super resolution, and analyzed the single-cell TAD structure at a 30 kb resolutions. MERFISH is used to prove directly that TAD domain is a physical structure that exists in the cell, and also find the high heterogeneity of TAD boundary (boundaries) in the single cell [19]. However, the resolution of 30 kb is not sufficient to see the subtle structure, such as the interaction between the tissue-specific enhancer and promoter. Mateo et al. has developed a new technology called ORCA, which combines MERFISH with STORM technology to increase the resolution of DNA detection to 2 kb, and clearly sees the interaction between enhancer and promoter. At the same time, nascent RNA was detected to study the effect of the interaction between enhancer and promoter on gene transcription [20]. With the help of ORCA, the 3D genome structure and the expression of gene can be simultaneously detected in thousands of embryos in situ or in single cell, which opens a new door for studying the mechanism of gene in cell differentiation, embryonic development, and disease occurrence. The drawback is that the flux of ORCA is relatively low, but if combined with Hi-C, it will be a good complement.

Targeted chromatin capture (T2C) techniques [21] and Capture-C [22] can be used for the interaction of interesting chromatin fragments, which use enzyme-digestive products and markers that are labeled with biotin probes to aggregate interesting chromatin fragments and allow them to interact. But they do not observe the interaction of whole-genome loci.

The above techniques do not allow specific chromatin to interact with specific proteins. ChIA-PET is a combination of 3C technology and ChIA (chromatin immunoprecipitation technology), which can select specific proteins to interact with specific chromatin to obtain specific chromatin mutual mapping [23].

In short, 3C technology and its derivative technologies have made a great contribution to 3D genomics. Now the hottest 3D genome technologies are dominated by the Hi-C series as well as the CHIA-PET series (Table 1 and Fig. 1).

Table 1 Chromosome conformation capture technology
Fig. 1
figure1

Flow chart of chromosome conformation capture technologies

The 4D nucleome project was proposed recently. The combination of Hi-C and imaging (MERFISH and ORCA) contributes to the development of the 4D nucleome project [24]. The 4D nucleome project has the following three goals: (a) understanding the 3D genome structure of mammalian nuclei on time and space. (b) Understanding how the expression of genes to achieve cellular function on time and space. (c) Understanding how the changes in nuclear structure affect growth and disease development in time and space. The 4D nucleome project adds time element based on the 3D genome. Therefore, to study the 4D nucleome project, it is necessary to develop dynamic imaging methods and integrate various data of 3D genomics.

Several 4D technologies have been developed. In imaging, CRISPR-Sirius greatly improves the sensitivity and diversity of tracking DNA [25], which lays a good foundation for studying how the dynamic structure of 3D genome to change on time element, how 3D genome to affect gene transcription and regulation, and how 3D genome to determine the fate of cells. In the field of data analysis, a method named FIND considers the spatial dependence between adjacent locations of DNA sequences and uses the principle of spatial Poisson distribution to find regions where chromatin interaction is evident [26]. Furthermore, the method does not analyze chromatin topological domain and chromatin ring of Hi-C data, directly detect more differential interaction locations, which are close to CTCF binding sites.

The structures and functions of the 3D genome

The characteristics of 3D genome structure

Chromatin can be divided into multilayer structures using 3D genome techniques: A/B blocks, TAD, and chromatin loops (Fig. 2a). In the nucleus, chromatin can be divided into AB blocks, A block is rich in active genes and open chromatin fragments, and B block contains inactive genes and closed chromatin fragments [27]. Chromatin blocks can be subdivided into TADs. TAD is formed by cohesin-mediated exo-chromosome extrusion [28]. TAD is not only stable in different cell types, but also highly conserved among species, indicating that TAD is an intrinsic property of the mammalian genome and its borders are insulator-binding proteins CTCF, housekeeping genes, and tRNA [29]. What is in TAD? TAD consists of chromatin loops. Chromatin loops provide a place where specific enhancers interact with genes and are essential for normal gene activation and inhibition. The chromatin loop forms a chromosomal scaffold that remains largely throughout development and is disrupted by genetic and epigenetic factors in the disease [30]. Chromatin loops are regions that are insulated by CTCF that act as spacers for gene transcription [31]. Chromatin loop is highly conserved in human chromatin, but it often changes when cancer occurs [32]. Therefore, studying the chromatin loop is helpful to reveal the law of disease occurrence. The nucleosome is the structure with the smallest known chromatin. How does it fold into a high-level structure step by step? Ohno et al. found two basic structural units composed of nucleosomes, α-tetrahedron and β-diamond [33] (Fig. 2a).

Fig. 2
figure2

The structures of 3D genome and analysis process. a Chromosomes can be divided into A/B blocks, topologically associated structural domains (TAD) (100–1000 kb), and chromatin loops (10–500 kb). b The analysis process of 3D genomics. c Boundary destruction leads to activation of proto-oncogenes

The functions of the 3D genome

The transcriptional regulation

The chromatin structure is closely intertwined with transcription regulation. Using the 3D genome technique, Huang et al. provided a global view of the characteristics of specific chromatin structures in the developmental stage of primary erythrocyte cells, and found a new key regulatory region HBBP1 [34]. HBBP1 region plays an important role in regulating bead protein transcription at the fetal and adult stages and can be a potential target for the treatment of hemoglobin disease. The development of brain is mysterious. Through chromosome conformation, Won et al. identified hundreds of new enhancer–promoter interactions during the period of brain development and revealed chromatin associated with brain tissue, providing information for the biological interpretation of complex genetic diseases and schizophrenia [35]. During adipogenesis and myogenesis, the dynamic change of three spatial structures (A/B, TAD, and differential chromatin interactions) is consistent with gene expression modulation [36]. Rennie et al. established a transcriptional decomposition approach to investigate and model the coupling between transcriptional activity, chromosomal positioning, and chromatin architecture, and verified chromosomal positioning and 3D chromatin architecture are tightly coupled with transcriptional regulation [37]. All of the above reveals that the 3D genome structure plays an important regulatory effect on transcription, and the 3D genome study can understand the growth and development process of the human body and the process of disease occurrence.

A key transcriptional regulator—CTCF

CTCF plays a central role in the structure and function of the 3D genome [30, 38]. CTCF-mediated chromatin interactions exhibit a wide range of changes in cells [39], which suggests that CTCF is a key transcription regulator and has a significant impact on transcription. CTCF divides chromatin into multiple regions with distinct epigenetic states and different transcriptional activities [40]. The following are the specific roles of CTCF: (a) it maintains the stability of telomeres and chromosomes. Beishline et al. found subtelomeric CTCF facilitates telomeric DNA replication by promoting TERRA transcription and demonstrate that CTCF-driven TERRA transcription acts in cis to facilitate telomere repeat replication and chromosome stability [41]. (b) It can promote the repair of double chain fracture of DNA. CTCF is recruited to sites of DNA damage, and promotes homologous recombination repair of DNA double-strand breaks with the help of DNA homologous recombination repair factors MDC1 and AGO2 [42]. (c) It helps form chromatin rings (loop). The CTCF–CTCF ring formed the chromosome framework of the insulated neighborhood, which in turn formed the topological associated domains (TADs) [32, 43]. (d) It participates in the transcription regulatory process. The CTCF/cohesin boundary in TAD is not only used as a barrier, but also divides the area to make enhancer interact with promoter [44]. (e) It has the function of an insulator. CTCF sites exist in all vertebrate booster blocking elements, and have the effect of preventing gene activation [45]. In addition to CTCF, there are many other transcription factors such as Sak1 [46], FOXP [47], KLF2 [48], and E2F1 [49].

Enhancer and super-enhancer

Through 3D genome, the global chromatin connectivity maps reveal ~40,000 long-range interactions and suggest precise enhancer–promoter associations [50], which indicates that 3D genome technology can detect enhancers in the whole genome. Super-enhancer consists of clusters of enhancers that are densely occupied by the master regulators and mediator, which is more stronger than enhancer in transcriptional regulation [51, 52]. Using 3D genome technology, it was found that super-enhancer is located within a loop connected by two interacting CTCF sites co-occupied by cohesin [53]. Super enhancers are cell type specific and mark key cell identity genes [51], and chromatin connectivity patterns around super enhancers and broad domains are nonstochastic and conserved across cell types and can be captured via different assays [54]. The 3D genome technologies not only can probe the structure of super enhancers, but also reveal the occurrence and development of diseases related to super enhancers. Variants in the super enhancers play an important role in controlling prototypical platelet function [55]. Focal amplification of super enhancers upregulate the expression of cancer-driving genes and is common in many cancers [56].

The 3D genome and precision medicine

The 3D genome and genome-wide association studies (GWAS)

GWAS have emerged as a powerful and successful tool to identify common human disease alleles by using high-throughput genotyping technology, which can analyze hundreds of thousands of single-nucleotide polymorphisms (SNPs) to identify associations with complex clinical conditions and phenotypic traits [57, 58]. SNPs are DNA sequence polymorphisms caused by variations in a single nucleotide at the genome level. The polymorphisms shown in SNPs involve only single-base variations, which can be caused by transition or transversion of a single base, or by insertion or deletion of a base. SNPs are the most common type of human heritable variation and can account for more than 80% of all known polymorphisms. Some SNPs can directly affect protein function and change biological characters. What is the relationship between SNPs and phenotypic traits? Phenotypic traits are expressed through physical interactions between genetic variants (SNPs) within chromatin regulatory circuitry [59]. How do you understand the physical interactions? Assuming there are five spots on a line of DNA called ABCDE, point A and point C can be combined into a complex, forming a circular structure containing point B. Due to the isolation effect of the ring structure, points B, D, and E cannot contact. If point C mutates (point C can be thought of as a SNP), point A will not bind to point C, but to point E, forming a new ring. Point B comes into contact with points D and E, which has an effect. There are innumerable sites on a linear DNA. Sites can combine with each other to form a spatial network. Thus, the SNPS at the far end can be connected through the site to produce the effect of contact. 3D genome helps us understand how genes corresponding to SNPs are expressed in a network. Through GWAS, a large number of SNPs related to type 2 diabetes (T2D) have been discovered. Using 3D genome, Schierding and O’Sullivan found a three-way functional–spatial connection between the TM6SF2, CTRB1–BCAR1, and CELSR2–PSRC1 loci (rs201189528, rs7202844, and rs7202844, respectively) connected through the KCNIP3 and BCAR1/BCAR3 loci, respectively [60]. These less relevant SNPs are linked together with the help of spatial hubs (KCNIP3 and BCAR1/BCAR3). Moreover, the related obesity SNPs are also related to the related T2D SNPs through the spatial network, which indicates that obesity is one of the factors for the occurrence of T2D [61]. Insulin is one of the treatments for T2D. However, enhancers of T2D risk variants can affect insulin-related target genes, leading to a decline in the role of insulin [62]. In addition to T2D, SNPs in other diseases show similar links such as glioma [63], chronic kidney disease [64], and acute lymphocytic leukemia [65]. All in all, GWAS finds SNPs of disease-related mutations, while 3D genome finds the spatial connections of SNPs.

The 3D genome and cancer

The 3D genome impacts cancer heterogeneity and evolution

Cancer heterogeneity refers to the fact that during the growth and proliferation of a cancer, cancer cells show different changes, leading to differences in the growth rate, invasion ability, sensitivity to drugs, prognosis, and other aspects of the cancer [66,67,68,69]. In other words, the cancer has evolved. Using single-cell sequencing and Hi-C to analyze the 3D genome in multiple myeloma, it was found that the 3D genome structure varies widely [70, 71], which shows that cancer heterogeneity is associated with changes in the 3D genome structures. In the cancer cells, gene-rich chromosomes as well as areas of open and highly transcribed chromatins are rearranged to greater spatial proximity, thus enabling genes to share transcriptional machinery and regulatory elements [72], which indicates that the cancer cells are in the process of high-speed translation. Moreover, the cancer TAD structure is smaller than normal cells [73]. Mutations produce a large number of oncogenes [74] coupled with changes in chromatin and TAD, which will form different combinations of enhancers and cancer genes, resulting in cancer.

Breast cancer

Through Hi-C, MCF-7 (breast cancer cells) has a more open compartmentalization than McF-10A (breast epithelial cells). Twelve percent of all compartments in the MCF-10A genome transitioned to the opposite compartment (B-type to A-type and vice versa) in MCF-7 cells. Because of the transition of compartments, interaction frequency between small chromosomes in MCF-7 cells decrease, but frequency of open compartmentalization on chr16–22 in MCF-7 cells is higher. The transition of compartments is related to the WNT signaling pathway, which is frequently implicated in tumorigenesis [75]. In another study of breast cancer, with the help of open compartmentalization, chromosomes as well as chromosomes are rearranged to greater spatial proximity, thus allowing genes to share transcriptional machinery and regulatory elements. At a smaller scale, differentially interacting loci are enriched for cancer proliferation and estrogen-related genes, causing breast cancer [72]. In a word, A/B compartmentalization is related to tumorigenesis.

Triple-negative breast cancer (TNBC) is more aggressive and metastatic than other breast cancers [76]. By 3D genome, Qiao et al. found a new way to epithelial-to-mesenchymal transition (EMT) about TNBC. The approach is based on the inflammatory cytokine TNF Alpha acting on PI3K/Akt and MAPK/ERK pathways to produce AP-1 signal, which mediates chromatin cycling to regulate the transcription and expression of EMT-related ZEB2 gene, thus promoting the invasion and metastasis of tumor cells [77]. In addition, Dryden et al. identified two SNPs rs12613955 and rs4442975 that could affect the FOXA1 expression in breast cancer [78], which mean risk loci may affect gene expression near and far.

Testicular germ cells cancer

With the help of Hi-C and GWAS, Litchfield et al. identified 19 risk loci associated with testicular germ cell tumors (TGCT). They also found that extensive disruption of developmental transcriptional regulators can serve as a basis for TGCT susceptibility and is consistent with failed primordial germ cells. Moreover, defective microtubule assembly and dysregulation of KIT-MAPK signaling also feature as recurrently disrupted pathways [79].

Prostate cancer

The discovery of TAD mutants is usually one of the key conditions for cancer. Hi-C analysis of prostate cancer cells showed that TAD was smaller and more abundant in the genome of cancer cells, and the boundaries of TADs were rich in CTCF and H3K4me3. The formation of new TAD boundary is related to the change of copy number variation (CNV). Tumor-specific interactions occurred in the smaller TAD, where enriched enhancers, promoters, and CTCF occupy genomic regions. Furthermore, changes in chromatin interactions over time are consistent with changes in epigenetic modifications and gene expression [73]. Rickman et al. found that overexpression of ERG (a cancer-causing transcription factor) is associated with changes in TAD in prostate cancer [80], suggesting that other cancer-causing transcription factors may also affect TAD and cause cancer.

Hematological cancer

The imbalance of insulin-like growth factor type I receptor (IGF1R) is related to the progression of malignant tumor and treatment resistance. Sun et al. found a new long noncoding RNA (lncRNA)–IRAIN, which can interact with chromatin DNA and participate in the formation of intrachromatic enhancers/promoter rings, playing a transcriptional control role. If IRAIN is downregulated, it will activate IGF1R gene and promote tumor growth [81].

Multiple myeloma

Since the first generation CNV map of the human genome has been published [82, 83], it has been confirmed that CNV is associated with tumors. Somatic copy number alterations (SCNAs) occur in a variety of tumors [84], especially as driver events in some specific cancers [85, 86]. Gene CNVs can be divided into large aberrations and small aberrations. Large aberrations can be loss or duplication of whole or part of chromosomes (which can be called aneuploidy) [87, 88]. Small aberrations can be a single-base mutation or insertion [89, 90]. In recent years, studies have found that changes in the CNV of 3D genome are related to tumorigenesis. Multiple myeloma is characterized by frequent chromosome copy number and chromatin translocation [91]. Using the 3D genome technology to study multiple myeloma, Wu et al. found that Hi-C data can be used to detect CNV, and it is easier to find chromatin translocation events by linking Hi-C data with WGS. The TAD of myeloma cells increased by 25% and the average length was smaller than that of normal cells. The key is that the CNV breakpoint and the TAD boundary are markedly integrated as a whole [71], indicating that CNVs may help to form a new TAD and affect gene expression, thereby forming a tumor. In a study by Franke et al., the destruction of the TAD boundary by SCNA led to the formation of a new TAD [92]. Moreover, disruption of the insulative neighborhood boundary (chromatin ring border) can result in oncogene activation in cancer cells [93]. The two findings confirm that CNV affects TAD and can lead to multiple myeloma.

Medulloblastoma

Which gene sequence activates the oncogene to cause cancer formation? The theory of “enhanced hijacking” was discovered during the study of medulloblastoma [94]. This theory mainly means that the 3D genome structure is destroyed, and the enhancer sequence in the chromatin is close to the oncogene to activate the oncogene to cause cancer. So how to verify that CNVs promote the phenomenon of “enhancer hijacking”? Recently, Weischenfeldt et al. invented a new method called CESAM [95]. CESAM can predict the function of SCNA compared with TADs based on unified genomic sequence, epigenetic and 3D genome data information [95]. The increase or decrease of SCNA affects the occurrence of transcription to some extent [96, 97]. Therefore, there is a doctrine to resolve this phenomenon, called the dose effect theory. Soh et al. found that SCNA–miRNA significantly altered the biological processes associated with cancer development [98], suggesting the importance of SCNA in cancer noncoding regions and participating in the expression of cancer biological behavior. CNVs not only affect the 3D genome structures of cancer, but also change the transcriptional regulation process, thus changing the biological behavior of individuals (Fig. 2c).

The 3D genome and nonneoplastic diseases

Structural variations are also steadily present in nonneoplastic diseases. TAD is highly conserved in the chromatin structure, and its border is rich in insulator-binding proteins CTCF, housekeeping genes, tRNA and SINE retrotransposons [29], which are the basic functional units of gene replication and transcription [99]. However, recent studies have also shown that during the entire cell cycle, TAD is a dynamic structure that is constantly forming and destroying [100], which may lead to erroneous TAD structures and lay the foundation for disease occurrence. TAD structural variations can be divided into internal structural variations and boundary structure variations (Fig. 3).

Fig. 3
figure3

TAD internal and boundary variations. a TAD internal variation. Enhancer A point mutation results in abnormal expression of gene A. Enhancer A-point replication results in abnormal expression of gene A. b TAD internal variation. Deletion of the enhancer A point results in the gene A not being expressed. Enhancer A point inversion does not affect gene A expression. c TAD boundary variation. Deletion of the TAD border results in TAD fusion leading to enhancer A affecting gene B expression. TAD border replication results in new TAD formation such that enhancer A affects gene B expression. d TAD boundary variation. Inversion of the TAD border results in the shift of enhancer A into the TAD containing gene B to affect gene B expression. Gene A is not expressed due to loss of enhancer A. e TAD boundary variation. Translocation occurs on chromosome 1 and chromosome 2. Gene A ran to chromosome 2. Enhancer D affects the expression of gene A

TAD internal structural variations

Shadow enhancers (away from target gene) and proximal primary enhancers (near target gene) act on the transcription expression activity of target gene [101, 102]. The point mutation of enhancer SIMO in TAD alters the function of the gene PAX6, leading to aniridia (a patient disease) [103]. IHH is the main gene for bone development, involved in cartilage cell differentiation, joint formation, and osteoblast differentiation. In TAD, enhancers replication of gene IHH lead to overexpression of IHH, causing abnormal phalanges, fusion of sutures and syndactyly [104]. Similar to gene IHH, enhancer replication of gene BMP2 can lead to the expression error of BMP2, which lead to a limb malformation feature by hypoplastic middle phalanges of the second and fifth fingers [105]. The SHH gene controls limb development. In TAD, point mutations and repetitions in the enhanced subsequence (ZRS) that control SHH cause unique limb phenotypes (multiple fingers) [106, 107]. Moreover, Lohan et al. found that ZRS replication more than 80 kb is related to Haas-type polysyndactyly (HTS) phenotype, while replication is less than 80 kb related to Laurin–Sandrow syndrome (LSS) phenotype [108]. LSS phenotype is more serious than HTS phenotype. The coding exon near the target gene acts as an enhanced subspecific enhancer [109], called exonic enhancer (eExons). In TAD, the absence of eExons located near the block of gene DYNC1I1 can lead to split hand/split foot deformity [110]. The point mutation, repetition, and deletion of the enhanced subsequence are all related to the occurrence of the disease, and the severity of the disease is related to the degree of change of the enhanced subsequence (Fig. 3a, b).

TAD boundary structure variations

Due to the highly conservative nature of TAD, the repeated formation of new TAD (neo-TAD) between two TADs does not have an expression effect. However, gene Kcnj2 and the enhancer of gene SOX each from two TADs appear in the neo-TAD, causing the error of expression of gene Kcnj2 [92]. The destruction of the TAD boundary causes the gene to interact with the enhancer in other TAD to produce disease [111, 112]. LMNB1 expresses a protein in the brain, and when its upstream sequence is missing, it destroys the TAD boundary, causing other enhancers of the brain to act on the LMNB1 promoter to trigger ADLD (autosomal dominant adult-onset demyelinating leukodystrophy) [113]. The inversion and translocation of chromosome destroy the TAD boundary and change the position of enhancers and genes. About 7.3% of the structural destruction of TAD is related to human innate abnormalities [114]. Inversion causes the enhancer sequence in TAD to move to another TAD, thus affecting its gene expression. Translocation causes the enhancer sequence in TAD on one chromosome to move into TAD on the other chromosome, affecting its gene expression [114]. TAD boundary duplication (deletion, translocation, and inversion) damages TAD structure and affect the occurrence of diseases (Fig. 3c–e).

The 3D genome and diagnosis

Hi-C is a tool for precise detection and characterization of chromosomal rearrangements and CNV in human tumors [115], seeking fusion genes that play a critical clinical role [116, 117]. FGFR2-CTNNA3 is the fusion gene of polymorphous low-grade neuroepithelial tumor of the young, which demonstrates oncogenic potential via MAPK/PI3K/mTOR pathway activation [118], and will be potential targets for clinical diagnosis and treatment. Gene fusions TMPRSS2-ERG are more common at the tissue level, which play a role to prevent prostate cancer [119]. NTRK gene fusions occur in many different tumor types. In certain rare tumors, they are present in most lesions, whereas in common cancers, the incidence may be 0.1–2% of tumors. Although they are not a specific tumor marker, they can be used as an auxiliary marker to diagnose tumors. Tyrosine kinase (TRK) inhibitor is a targeted drug for NTRK gene fusions [120]. In short, fusion genes can play a good role in early diagnosis of tumors and are potential therapeutic targets.

The 3D genome and treatment

The occurrence of a tumor depends on gene variation, and its genetic structure is very different from the genetic structure of normal cells [121]. Therefore, the analysis of clinical tumors generally requires two kinds of cells, one is normal cells and the other is tumor cells. There are three steps to the check step. The first step is to analyze the 3D genome structure of the two cells with Hi-C and WGS, compare their differences, and find out the structural variation points of the tumor gene. In the second step, the transcription of two kinds of cells was analyzed by RNA-seq, and how the mutation of gene structure affected the expression of tumor cells. The third step is to compare the collection data of the two cells with the public database to verify the analysis. In this way, the target of the drug can be understood so as to study the drug to treat the tumors (Fig. 2b).

Prostate cancer

UBE2C is the oncogene for prostate cancer. With the help of FOXA1 and phosphorylated MED1, which is produced through the PI3K/AKT/phosphorylated MED1 pathway, the long-distance interaction between UBE2C enhancer and promoter causes the overexpression of UBE2C gene [122]. In order to prevent the overexpression of UBE2C gene, the potential drugs carvacrol [123], ipatasertib [124], and abiraterone acetate [125] may be applied, mainly acting on the PI3K/AKT/phosphorylated MED1 pathway. There are also two other possible pathways to prostate cancer. One is that overexpression of ERG (an oncogenic transcription factor frequently overexpressed in prostate cancer as a result of a gene fusion) helps to form new chromatin rings and activates prostate cancer target genes in the chromatin ring [80]. In the other, the genes PRNCR1 and PCGEM1 overexpress two lncRNAs. Androgen receptor (AR) binding to lncRNAs and enhancers and promoters of prostate cancer genes create a chromosomal loop that allows RNA polymerase II to track from the enhancer to the promoter, which can lead to prostate cancer [126, 127]. The drug target ERG binding domain of the former pathway may be blocked by ERG inhibitory peptides [128], and the drug target AR of the latter pathway may be blocked by Sigma1 inhibitor [129], ASC-J9 [130], cisplatin [131], niclosamide [132], and EPI-001 [133].

Triple-negative breast cancer

Mechanism of TNBC is based on the inflammatory cytokine TNF alpha acting on PI3K/Akt and MAPK/ERK pathways to produce AP-1 signal, which mediates chromatin cycling to regulate the transcription and expression of EMT-related ZEB2 gene, promoting the invasion and metastasis of tumor cells [77]. AP-1, ZEB2, and ERK/Akt signaling can be regarded as potential drug targets. The AP-1 inhibitors may be AP-1-C301-S and SR 11302 [134]; ERK/Akt inhibiting drugs may be valproic acid and U0126 [135]; ZEB2′ target drug may be miR-132 [136]. Because of synergies, combination drugs tend to have a greater anticancer effect than a single drug. How to choose a combination of drugs requires a certain process. GWAS recognizes epistatic disease genes, and through 3D genome, the epistatic disease genes were filtered and were more closely related to disease. Finally, through cytotoxicity tests, the combination drug is screened. Five groups—dasatinib + vorinostat, gefitinib + vorinostat, cladribine + dasatinib, gefitinib + dasatinib, and cladribine + gefitinib—exhibited synergistic effects on anti-MCF-7 cells (breast cancer cell) [137].

Adenoid cystic carcinoma (ACC)

Chromosomal translocations are a common event in cancer. ACCMYB-TGFBR3 translocation places the super-enhancer BRD4 in contact with the promoter of MYB, which forms a new chromatin ring that drives the overexpression of oncogenic transcription factor MYB [138]. This is the main mechanism of ACC recurrence. JQ1 may be a potential drug for ACC and may antagonize super-enhancer BRD4 [139]. Cisplatin may be a target drug for prostate cancer [131] as well as TNBC [140]. The same drug can treat different cancers, indicating probably that the occurrence of cancer has a common way. For example, telomere changes in cancer cells may be common in all cancers. XPO1 inhibitors preferentially destroy 3D nuclear tissues of telomeres in tumor cells and are expected to become one of the necessary drugs for cancer [141].

Conclusions and perspectives

The 3D genomics has provided sufficient impetus for the study of the structure and function of DNA. However, the technology still has some limitations. Some technologies, such as Hi-C, are complex and costly to operate, making it difficult for ordinary laboratories to conduct research, so more convenient and cheaper technologies must be developed. The 3D genomics is a diverse technology that yields wildly different data. However, there is no uniform criterion for the analysis of these data. For this reason, a platform must be established, one that can verify data through high-throughput analysis. Such a platform cannot only improve the accuracy of data, improve various hypotheses related to 3D genomics, and improve the quality and standards of research, but also improve the efficiency, facilitate information exchange among scientists, and complete data integration. Some software can have certain advantages for data analysis and integration, but its user interface is too complex, so it must be simplified to facilitate the operation of researchers.

The proposed 4D nucleome project has pointed the way for the development of 3D genomics. We believe the advances in 3D genomics will be conducive to the development of the 4D nucleome project. Once we have a clear understanding of cell DNA structure, transcription, and expression, we will untangle the rules of biological growth and development and the preconditions for the occurrence of diseases, and promote the development of medical undertakings.

References

  1. 1.

    International Human Genome Sequencing C. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931–45.

  2. 2.

    Consortium EP. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004;306:636–40.

  3. 3.

    Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295:1306–11.

  4. 4.

    Gozzetti A, Le Beau MM. Fluorescence in situ hybridization: uses and limitations. Semin Hematol. 2000;37:320–33.

  5. 5.

    Ou HD, Phan S, Deerinck TJ, Thor A, Ellisman MH, O’Shea CC. ChromEMT: Visualizing 3D chromatin structure and compaction in interphase and mitotic cells. Science. 2017;357. pii: eaag0025. https://doi.org/10.1126/science.aag0025.

  6. 6.

    Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, et al. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet. 2006;38:1348–54.

  7. 7.

    Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006;16:1299–309.

  8. 8.

    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93.

  9. 9.

    Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159:1665–80.

  10. 10.

    Lin D, Hong P, Zhang S, Xu W, Jamal M, Yan K, et al. Digestion-ligation-only Hi-C is an efficient and cost-effective method for chromosome conformation capture. Nat Genet. 2018;50:754–63.

  11. 11.

    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502:59–64.

  12. 12.

    Nagano T, Lubling Y, Varnai C, Dudley C, Leung W, Baran Y, et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature. 2017;547:61–7.

  13. 13.

    Ramani V, Deng X, Qiu R, Gunderson KL, Steemers FJ, Disteche CM, et al. Massively multiplex single-cell Hi-C. Nat Methods. 2017;14:263–6.

  14. 14.

    Xu Y, Zhou X. Applications of Single-Cell Sequencing for Multiomics. Methods Mol Biol. 2018;1754:327–74.

  15. 15.

    Stevens TJ, Lando D, Basu S, Atkinson LP, Cao Y, Lee SF, et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature. 2017;544:59–64.

  16. 16.

    Tang X, Huang Y, Lei J, Luo H, Zhu X. The single-cell sequencing: new developments and medical applications. Cell Biosci. 2019;9:53.

  17. 17.

    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348:aaa6090.

  18. 18.

    Wang S, Su JH, Beliveau BJ, Bintu B, Moffitt JR, Wu CT, et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science. 2016;353:598–602.

  19. 19.

    Bintu B, Mateo LJ, Su JH, Sinnott-Armstrong NA, Parker M, Kinrot S, et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science. 2018;362. pii: eaau1783. https://doi.org/10.1126/science.aau1783.

  20. 20.

    Mateo LJ, Murphy SE, Hafner A, Cinquini IS, Walker CA, Boettiger AN. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature. 2019;568:49–54.

  21. 21.

    Kolovos P, van de Werken HJ, Kepper N, Zuin J, Brouwer RW, Kockx CE, et al. Targeted Chromatin Capture (T2C): a novel high resolution high throughput method to detect genomic interactions and regulatory elements. Epigenetics Chromatin. 2014;7:10.

  22. 22.

    Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat Genet. 2014;46:205–12.

  23. 23.

    Li X, Luo OJ, Wang P, Zheng M, Wang D, Piecuch E, et al. Long-read ChIA-PET for base-pair-resolution mapping of haplotype-specific chromatin interactions. Nat Protoc. 2017;12:899–915.

  24. 24.

    Dekker J, Belmont AS, Guttman M, Leshyk VO, Lis JT, Lomvardas S, et al. The 4D nucleome project. Nature. 2017;549:219–26.

  25. 25.

    Ma H, Tu LC, Naseri A, Chung YC, Grunwald D, Zhang S, et al. CRISPR-Sirius: RNA scaffolds for signal amplification in genome imaging. Nat Methods. 2018;15:928–31.

  26. 26.

    Djekidel MN, Chen Y, Zhang MQ. FIND: difFerential chromatin INteractions Detection using a spatial Poisson process. Genome Res. 2018;28:412–22.

  27. 27.

    Naumova N, Dekker J. Integrating one-dimensional and three-dimensional maps of genomes. J Cell Sci. 2010;123:1979–88.

  28. 28.

    Sanborn AL, Rao SS, Huang SC, Durand NC, Huntley MH, Jewett AI, et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc Natl Acad Sci USA. 2015;112:E6456–65.

  29. 29.

    Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485:376–80.

  30. 30.

    Hnisz D, Day DS, Young RA. Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control. Cell. 2016;167:1188–200.

  31. 31.

    Tang Z, Luo OJ, Li X, Zheng M, Zhu JJ, Szalaj P, et al. CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription. Cell. 2015;163:1611–27.

  32. 32.

    Ji X, Dadon DB, Powell BE, Fan ZP, Borges-Rivera D, Shachar S, et al. 3D Chromosome Regulatory Landscape of Human Pluripotent Cells. Cell Stem Cell. 2016;18:262–75.

  33. 33.

    Ohno M, Ando T, Priest DG, Kumar V, Yoshida Y, Taniguchi Y. Sub-nucleosomal Genome Structure Reveals Distinct Nucleosome Folding Motifs. Cell. 2019;176:520–34. e25.

  34. 34.

    Huang P, Keller CA, Giardine B, Grevet JD, Davies JOJ, Hughes JR, et al. Comparative analysis of three-dimensional chromosomal architecture identifies a novel fetal hemoglobin regulatory element. Genes Dev. 2017;31:1704–13.

  35. 35.

    Won H, de la Torre-Ubieta L, Stein JL, Parikshak NN, Huang J, Opland CK, et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature. 2016;538:523–7.

  36. 36.

    He M, Li Y, Tang Q, Li D, Jin L, Tian S, et al. Genome-Wide Chromatin Structure Changes During Adipogenesis and Myogenesis. Int J Biol Sci. 2018;14:1571–85.

  37. 37.

    Rennie S, Dalby M, van Duin L, Andersson R. Transcriptional decomposition reveals active chromatin architectures and cell specific regulatory interactions. Nat Commun. 2018;9:487.

  38. 38.

    Ghirlando R, Felsenfeld G. CTCF: making the right connections. Genes Dev. 2016;30:881–91.

  39. 39.

    Kai Y, Andricovich J, Zeng Z, Zhu J, Tzatsos A, Peng W. Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features. Nat Commun. 2018;9:4221.

  40. 40.

    Handoko L, Xu H, Li G, Ngan CY, Chew E, Schnapp M, et al. CTCF-mediated functional chromatin interactome in pluripotent cells. Nat Genet. 2011;43:630–8.

  41. 41.

    Beishline K, Vladimirova O, Tutton S, Wang Z, Deng Z, Lieberman PM. CTCF driven TERRA transcription facilitates completion of telomere DNA replication. Nat Commun. 2017;8:2114.

  42. 42.

    Lang F, Li X, Zheng W, Li Z, Lu D, Chen G, et al. CTCF prevents genomic instability by promoting homologous recombination-directed DNA double-strand break repair. Proc Natl Acad Sci USA. 2017;114:10912–7.

  43. 43.

    Zhu X, Gao S, Luo H, Fan W, Guo S, Yao H, et al. 9q33.3, a stress-related chromosome region, contributes to reducing lung squamous cell carcinoma risk. J Thorac Oncol. 2014;9:1041–7.

  44. 44.

    Hanssen LLP, Kassouf MT, Oudelaar AM, Biggs D, Preece C, Downes DJ, et al. Tissue-specific CTCF-cohesin-mediated chromatin architecture delimits enhancer interactions and function in vivo. Nat Cell Biol. 2017;19:952–61.

  45. 45.

    Bell AC, West AG, Felsenfeld G. The protein CTCF is required for the enhancer blocking activity of vertebrate insulators. Cell. 1999;98:387–96.

  46. 46.

    Garg A, Futcher B, Leatherwood J. A new transcription factor for mitosis: in Schizosaccharomyces pombe, the RFX transcription factor Sak1 works with forkhead factors to regulate mitotic expression. Nucleic Acids Res. 2015;43:6874–88.

  47. 47.

    Estruch SB, Graham SA, Quevedo M, Vino A, Dekkers DHW, Deriziotis P, et al. Proteomic analysis of FOXP proteins reveals interactions between cortical transcription factors associated with neurodevelopmental disorders. Hum Mol Genet. 2018;27:1212–27.

  48. 48.

    Lee JY, Skon CN, Lee YJ, Oh S, Taylor JJ, Malhotra D, et al. The transcription factor KLF2 restrains CD4(+) T follicular helper cell differentiation. Immunity. 2015;42:252–64.

  49. 49.

    Xie Q, Peng S, Tao L, Ruan H, Yang Y, Li TM, et al. E2F transcription factor 1 regulates cellular and organismal senescence by inhibiting Forkhead box O transcription factors. J Biol Chem. 2014;289:34205–13.

  50. 50.

    Zhang Y, Wong CH, Birnbaum RY, Li G, Favaro R, Ngan CY, et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature. 2013;504:306–10.

  51. 51.

    Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307–19.

  52. 52.

    Tang J, Xu Z, Huang L, Luo H, Zhu X. Transcriptional regulation in model organisms: recent progress and clinical implications. Open Biol. 2019;9:190183.

  53. 53.

    Dowen JM, Fan ZP, Hnisz D, Ren G, Abraham BJ, Zhang LN, et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell. 2014;159:374–87.

  54. 54.

    Thibodeau A, Marquez EJ, Shin DG, Vera-Licona P, Ucar D. Chromatin interaction networks revealed unique connectivity patterns of broad H3K4me3 domains and super enhancers in 3D chromatin. Sci Rep. 2017;7:14466.

  55. 55.

    Petersen R, Lambourne JJ, Javierre BM, Grassi L, Kreuzhuber R, Ruklisa D, et al. Platelet function is modified by common sequence variation in megakaryocyte super enhancers. Nat Commun. 2017;8:16058.

  56. 56.

    Zhang X, Choi PS, Francis JM, Imielinski M, Watanabe H, Cherniack AD, et al. Identification of focally amplified lineage-specific super-enhancers in human epithelial cancers. Nat Genet. 2016;48:176–82.

  57. 57.

    Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6.

  58. 58.

    Liang B, Ding H, Huang L, Luo H, Zhu X. GWAS in cancer: progress and challenges. Mol Genet Genom. 2020. https://doi.org/10.1007/s00438-020-01647-z [Epub ahead of print].

  59. 59.

    Corradin O, Cohen AJ, Luppino JM, Bayles IM, Schumacher FR, Scacheri PC. Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry. Nat Genet. 2016;48:1313–20.

  60. 60.

    Schierding W, O'Sullivan JM. Connecting SNPs in Diabetes: A Spatial Analysis of Meta-GWAS Loci. Front Endocrinol (Lausanne). 2015;6:102.

  61. 61.

    Fadason T, Ekblad C, Ingram JR, Schierding WS, O'Sullivan JM. Physical Interactions and Expression Quantitative Traits Loci Identify Regulatory Connections for Obesity and Type 2 Diabetes Associated SNPs. Front Genet. 2017;8:150.

  62. 62.

    Miguel-Escalada I, Bonas-Guarch S, Cebola I, Ponsa-Cobas J, Mendieta-Esteban J, Atla G, et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat Genet. 2019;51:1137–48.

  63. 63.

    Baskin R, Woods NT, Mendoza-Fandino G, Forsyth P, Egan KM, Monteiro AN. Functional analysis of the 11q23.3 glioma susceptibility locus implicates PHLDB1 and DDX6 in glioma susceptibility. Sci Rep. 2015;5:17367.

  64. 64.

    Brandt MM, Meddens CA, Louzao-Martinez L, van den Dungen NAM, Lansu NR, Nieuwenhuis EES, et al. Chromatin Conformation Links Distal Target Genes to CKD Loci. J Am Soc Nephrol. 2018;29:462–76.

  65. 65.

    Vijayakrishnan J, Qian M, Studd JB, Yang W, Kinnersley B, Law PJ, et al. Identification of four novel associations for B-cell acute lymphoblastic leukaemia risk. Nat Commun. 2019;10:5348.

  66. 66.

    Wang DC, Wang X. Systems heterogeneity: An integrative way to understand cancer heterogeneity. Semin Cell Dev Biol. 2017;64:1–4.

  67. 67.

    Zhu X, Luo H, Xu Y. Transcriptome analysis reveals an important candidate gene involved in both nodal metastasis and prognosis in lung adenocarcinoma. Cell Biosci. 2019;9:92.

  68. 68.

    Tan S, Li D, Zhu X. Cancer immunotherapy: Pros, cons and beyond. Biomed Pharmacother. 2020;124:109821.

  69. 69.

    Liang X, Li D, Leng S, Zhu X. RNA-based pharmacotherapy for tumors: From bench to clinic and back. Biomed Pharmacother. 2020;125:109997.

  70. 70.

    Gupta A, Place M, Goldstein S, Sarkar D, Zhou S, Potamousis K, et al. Single-molecule analysis reveals widespread structural variation in multiple myeloma. Proc Natl Acad Sci USA. 2015;112:7689–94.

  71. 71.

    Wu P, Li T, Li R, Jia L, Zhu P, Liu Y, et al. 3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations. Nat Commun. 2017;8:1937.

  72. 72.

    Mourad R, Hsu PY, Juan L, Shen C, Koneru P, Lin H, et al. Estrogen induces global reorganization of chromatin structure in human breast cancer cells. PLoS ONE. 2014;9:e113354.

  73. 73.

    Taberlay PC, Achinger-Kawecka J, Lun AT, Buske FA, Sabir K, Gould CM, et al. Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations. Genome Res. 2016;26:719–31.

  74. 74.

    Polak P, Karlic R, Koren A, Thurman R, Sandstrom R, Lawrence M, et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature. 2015;518:360–4.

  75. 75.

    Barutcu AR, Lajoie BR, McCord RP, Tye CE, Hong D, Messier TL, et al. Chromatin interaction analysis reveals changes in small chromosome and telomere clustering between epithelial and breast cancer cells. Genome Biol. 2015;16:214.

  76. 76.

    Tang Z, Li D, Hou S, Zhu X. The cancer exosomes: Clinical implications, applications and challenges. Int J Cancer. 2019. https://doi.org/10.1002/ijc.32762 [Epub ahead of print].

  77. 77.

    Qiao Y, Shiue CN, Zhu J, Zhuang T, Jonsson P, Wright AP, et al. AP-1-mediated chromatin looping regulates ZEB2 transcription: new insights into TNFalpha-induced epithelial-mesenchymal transition in triple-negative breast cancer. Oncotarget. 2015;6:7804–14.

  78. 78.

    Dryden NH, Broome LR, Dudbridge F, Johnson N, Orr N, Schoenfelder S, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 2014;24:1854–68.

  79. 79.

    Litchfield K, Levy M, Orlando G, Loveday C, Law PJ, Migliorini G, et al. Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor. Nat Genet. 2017;49:1133–40.

  80. 80.

    Rickman DS, Soong TD, Moss B, Mosquera JM, Dlabal J, Terry S, et al. Oncogene-mediated alterations in chromatin conformation. Proc Natl Acad Sci USA. 2012;109:9083–8.

  81. 81.

    Sun J, Li W, Sun Y, Yu D, Wen X, Wang H, et al. A novel antisense long noncoding RNA within the IGF1R gene locus is imprinted in hematopoietic malignancies. Nucleic Acids Res. 2014;42:9588–601.

  82. 82.

    Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, et al. Global variation in copy number in the human genome. Nature. 2006;444:444–54.

  83. 83.

    Li K, Luo H, Huang L, Luo H, Zhu X. Microsatellite instability: a review of what the oncologist should know. Cancer Cell Int. 2020;20:16.

  84. 84.

    Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899–905.

  85. 85.

    Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C. Emerging landscape of oncogenic signatures across human cancers. Nat Genet. 2013;45:1127–33.

  86. 86.

    Liu J, Li D, Luo H, Zhu X. Circular RNAs: The star molecules in cancer. Mol Aspects Med. 2019;70:141–52.

  87. 87.

    Safavi S, Forestier E, Golovleva I, Barbany G, Nord KH, Moorman AV, et al. Loss of chromosomes is the primary event in near-haploid and low-hypodiploid acute lymphoblastic leukemia. Leukemia. 2013;27:248–50.

  88. 88.

    Sarhadi VK, Lahti L, Scheinin I, Tyybakinoja A, Savola S, Usvasalo A, et al. Targeted resequencing of 9p in acute lymphoblastic leukemia yields concordant results with array CGH and reveals novel genomic alterations. Genomics. 2013;102:182–8.

  89. 89.

    Ha G, Roth A, Lai D, Bashashati A, Ding J, Goya R, et al. Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer. Genome Res. 2012;22:1995–2007.

  90. 90.

    Guo B, Li D, Du L, Zhu X: piRNAs: biogenesis and their potential roles in cancer. Cancer Metastasis Rev. 2020. https://doi.org/10.1007/s10555-020-09863-0 [Epub ahead of print].

  91. 91.

    Kamada Y, Sakata-Yanagimoto M, Sanada M, Sato-Otsubo A, Enami T, Suzukawa K, et al. Identification of unbalanced genome copy number abnormalities in patients with multiple myeloma by single-nucleotide polymorphism genotyping microarray analysis. Int J Hematol. 2012;96:492–500.

  92. 92.

    Franke M, Ibrahim DM, Andrey G, Schwarzer W, Heinrich V, Schopflin R, et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature. 2016;538:265–9.

  93. 93.

    Hnisz D, Weintraub AS, Day DS, Valton AL, Bak RO, Li CH, et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science. 2016;351:1454–8.

  94. 94.

    Northcott PA, Lee C, Zichner T, Stutz AM, Erkek S, Kawauchi D, et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature. 2014;511:428–34.

  95. 95.

    Weischenfeldt J, Dubash T, Drainas AP, Mardin BR, Chen Y, Stutz AM, et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat Genet. 2017;49:65–74.

  96. 96.

    Wei Y, Yan Z, Wu C, Zhang Q, Zhu Y, Li K, et al. Integrated analysis of dosage effect lncRNAs in lung adenocarcinoma based on comprehensive network. Oncotarget. 2017;8:71430–46.

  97. 97.

    Zhu X, Zhang J, Fan W, Wang F, Yao H, Wang Z, et al. The rs391957 variant cis-regulating oncogene GRP78 expression contributes to the risk of hepatocellular carcinoma. Carcinogenesis. 2013;34:1273–80.

  98. 98.

    Soh J, Cho H, Choi CH, Lee H. Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer. Cancers (Basel). 2018;10:12.

  99. 99.

    Pope BD, Ryba T, Dileep V, Yue F, Wu W, Denas O, et al. Topologically associating domains are stable units of replication-timing regulation. Nature. 2014;515:402–5.

  100. 100.

    Hansen AS, Cattoglio C, Darzacq X, Tjian R. Recent evidence that TADs and chromatin loops are dynamic structures. Nucleus. 2018;9:20–32.

  101. 101.

    Hong JW, Hendrix DA, Levine MS. Shadow enhancers as a source of evolutionary novelty. Science. 2008;321:1314.

  102. 102.

    Perry MW, Boettiger AN, Bothma JP, Levine M. Shadow enhancers foster robustness of Drosophila gastrulation. Curr Biol. 2010;20:1562–7.

  103. 103.

    Bhatia S, Bengani H, Fish M, Brown A, Divizia MT, de Marco R, et al. Disruption of autoregulatory feedback by a mutation in a remote, ultraconserved PAX6 enhancer causes aniridia. Am J Hum Genet. 2013;93:1126–34.

  104. 104.

    Will AJ, Cova G, Osterwalder M, Chan WL, Wittler L, Brieske N, et al. Composition and dosage of a multipartite enhancer cluster control developmental expression of Ihh (Indian hedgehog). Nat Genet. 2017;49:1539–45.

  105. 105.

    Dathe K, Kjaer KW, Brehm A, Meinecke P, Nurnberg P, Neto JC, et al. Duplications involving a conserved regulatory element downstream of BMP2 are associated with brachydactyly type A2. Am J Hum Genet. 2009;84:483–92.

  106. 106.

    Sun M, Ma F, Zeng X, Liu Q, Zhao XL, Wu FX, et al. Triphalangeal thumb-polysyndactyly syndrome and syndactyly type IV are caused by genomic duplications involving the long range, limb-specific SHH enhancer. J Med Genet. 2008;45:589–95.

  107. 107.

    Klopocki E, Ott CE, Benatar N, Ullmann R, Mundlos S, Lehmann K. A microduplication of the long range SHH limb regulator (ZRS) is associated with triphalangeal thumb-polysyndactyly syndrome. J Med Genet. 2008;45:370–5.

  108. 108.

    Lohan S, Spielmann M, Doelken SC, Flottmann R, Muhammad F, Baig SM, et al. Microduplications encompassing the Sonic hedgehog limb enhancer ZRS are associated with Haas-type polysyndactyly and Laurin-Sandrow syndrome. Clin Genet. 2014;86:318–25.

  109. 109.

    Birnbaum RY, Clowney EJ, Agamy O, Kim MJ, Zhao J, Yamanaka T, et al. Coding exons function as tissue-specific enhancers of nearby genes. Genome Res. 2012;22:1059–68.

  110. 110.

    Tayebi N, Jamsheer A, Flottmann R, Sowinska-Seidler A, Doelken SC, Oehl-Jaschkowitz B, et al. Deletions of exons with regulatory activity at the DYNC1I1 locus are associated with split-hand/split-foot malformation: array CGH screening of 134 unrelated families. Orphanet J Rare Dis. 2014;9:108.

  111. 111.

    Lupianez DG, Kraft K, Heinrich V, Krawitz P, Brancati F, Klopocki E, et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell. 2015;161:1012–25.

  112. 112.

    Mishra A, Hawkins RD. Three-dimensional genome architecture and emerging technologies: looping in disease. Genome Med. 2017;9:87.

  113. 113.

    Giorgio E, Robyr D, Spielmann M, Ferrero E, Di Gregorio E, Imperiale D, et al. A large genomic deletion leads to enhancer adoption by the lamin B1 gene: a second path to autosomal dominant adult-onset demyelinating leukodystrophy (ADLD). Hum Mol Genet. 2015;24:3143–54.

  114. 114.

    Redin C, Brand H, Collins RL, Kammin T, Mitchell E, Hodge JC, et al. The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nat Genet. 2017;49:36–45.

  115. 115.

    Harewood L, Kishore K, Eldridge MD, Wingett S, Pearson D, Schoenfelder S, et al. Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumours. Genome Biol. 2017;18:125.

  116. 116.

    Parker BC, Zhang W. Fusion genes in solid tumors: an emerging target for cancer diagnosis and treatment. Chin J Cancer. 2013;32:594–603.

  117. 117.

    Kaye FJ. Mutation-associated fusion cancer genes in solid tumors. Mol Cancer Ther. 2009;8:1399–408.

  118. 118.

    Surrey LF, Jain P, Zhang B, Straka J, Zhao X, Harding BN, et al. Genomic Analysis of Dysembryoplastic Neuroepithelial Tumor Spectrum Reveals a Diversity of Molecular Alterations Dysregulating the MAPK and PI3K/mTOR Pathways. J Neuropathol Exp Neurol. 2019;78:1100–11.

  119. 119.

    Tomlins SA, Day JR, Lonigro RJ, Hovelson DH, Siddiqui J, Kunju LP, et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. 2016;70:45–53.

  120. 120.

    Hsiao SJ, Zehir A, Sireci AN, Aisner DL. Detection of Tumor NTRK Gene Fusions to Identify Patients Who May Benefit from Tyrosine Kinase (TRK) Inhibitor Therapy. J Mol Diagn. 2019;21:553–71.

  121. 121.

    Zhu X, Lin MCM, Fan W, Tian L, Wang J, Ng SS, et al. An intronic polymorphism in GRP78 improves chemotherapeutic prediction in non-small cell lung cancer. Chest. 2012;141:1466–72.

  122. 122.

    Chen Z, Zhang C, Wu D, Chen H, Rorick A, Zhang X, et al. Phospho-MED1-enhanced UBE2C locus looping drives castration-resistant prostate cancer growth. EMBO J. 2011;30:2405–19.

  123. 123.

    Luo Y, Wu JY, Lu MH, Shi Z, Na N, Di JM. Carvacrol Alleviates Prostate Cancer Cell Proliferation, Migration, and Invasion through Regulation of PI3K/Akt and MAPK Signaling Pathways. Oxid Med Cell Longev. 2016;2016:1469693.

  124. 124.

    Saura C, Roda D, Rosello S, Oliveira M, Macarulla T, Perez-Fidalgo JA, et al. A First-in-Human Phase I Study of the ATP-Competitive AKT Inhibitor Ipatasertib Demonstrates Robust and Safe Targeting of AKT in Patients with Solid Tumors. Cancer Discov. 2017;7:102–13.

  125. 125.

    Wei XX, Hsieh AC, Kim W, Friedlander T, Lin AM, Louttit M, et al. A Phase I Study of Abiraterone Acetate Combined with BEZ235, a Dual PI3K/mTOR Inhibitor, in Metastatic Castration Resistant Prostate Cancer. Oncologist. 2017;22:503–e43.

  126. 126.

    Wang Q, Carroll JS, Brown M. Spatial and temporal recruitment of androgen receptor and its coactivators involves chromosomal looping and polymerase tracking. Mol Cell. 2005;19:631–42.

  127. 127.

    Yang L, Lin C, Jin C, Yang JC, Tanasa B, Li W, et al. lncRNA-dependent mechanisms of androgen-receptor-regulated gene activation programs. Nature. 2013;500:598–602.

  128. 128.

    Wang X, Qiao Y, Asangani IA, Ateeq B, Poliakov A, Cieslik M, et al. Development of Peptidomimetic Inhibitors of the ERG Gene Fusion Product in Prostate Cancer. Cancer Cell. 2017;31:532–48. e7.

  129. 129.

    Thomas JD, Longen CG, Oyer HM, Chen N, Maher CM, Salvino JM, et al. Sigma1 Targeting to Suppress Aberrant Androgen Receptor Signaling in Prostate Cancer. Cancer Res. 2017;77:2439–52.

  130. 130.

    Lai KP, Huang CK, Chang YJ, Chung CY, Yamashita S, Li L, et al. New therapeutic approach to suppress castration-resistant prostate cancer using ASC-J9 via targeting androgen receptor in selective prostate cells. Am J Pathol. 2013;182:460–73.

  131. 131.

    Grimison PS, Stockler MR, Chatfield M, Thomson DB, Gebski V, Friedlander M, et al. Accelerated BEP for metastatic germ cell tumours: a multicenter phase II trial by the Australian and New Zealand Urogenital and Prostate Cancer Trials Group (ANZUP). Ann Oncol. 2014;25:143–8.

  132. 132.

    Liu C, Armstrong C, Zhu Y, Lou W, Gao AC. Niclosamide enhances abiraterone treatment via inhibition of androgen receptor variants in castration resistant prostate cancer. Oncotarget. 2016;7:32210–20.

  133. 133.

    Myung JK, Banuelos CA, Fernandez JG, Mawji NR, Wang J, Tien AH, et al. An androgen receptor N-terminal domain antagonist for treating prostate cancer. J Clin Invest. 2013;123:2948–60.

  134. 134.

    Gonzalez-Rubio S, Linares CI, Aguilar-Melero P, Rodriguez-Peralvarez M, Montero-Alvarez JL, de la Mata M, et al. AP-1 Inhibition by SR 11302 Protects Human Hepatoma HepG2 Cells from Bile Acid-Induced Cytotoxicity by Restoring the NOS-3 Expression. PLoS ONE. 2016;11:e0160525.

  135. 135.

    Zhang C, Liu S, Yuan X, Hu Z, Li H, Wu M, et al. Valproic Acid Promotes Human Glioma U87 Cells Apoptosis and Inhibits Glycogen Synthase Kinase-3beta Through ERK/Akt Signaling. Cell Physiol Biochem. 2016;39:2173–85.

  136. 136.

    Zheng YB, Luo HP, Shi Q, Hao ZN, Ding Y, Wang QS, et al. miR-132 inhibits colorectal cancer invasion and metastasis via directly targeting ZEB2. World J Gastroenterol. 2014;20:6515–22.

  137. 137.

    Quan Y, Liu MY, Liu YM, Zhu LD, Wu YS, Luo ZH, et al. Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes. Molecules. 2018;23:4.

  138. 138.

    Drier Y, Cotton MJ, Williamson KE, Gillespie SM, Ryan RJ, Kluk MJ, et al. An oncogenic MYB feedback loop drives alternate cell fates in adenoid cystic carcinoma. Nat Genet. 2016;48:265–72.

  139. 139.

    Ember SW, Lambert QT, Berndt N, Gunawan S, Ayaz M, Tauro M, et al. Potent Dual BET Bromodomain-Kinase Inhibitors as Value-Added Multitargeted Chemical Probes and Cancer Therapeutics. Mol Cancer Ther. 2017;16:1054–67.

  140. 140.

    Brisard D, Eckerdt F, Marsh LA, Blyth GT, Jain S, Cristofanilli M, et al. Antineoplastic effects of selective CDK9 inhibition with atuveciclib on cancer stem-like cells in triple-negative breast cancer. Oncotarget. 2018;9:37305–18.

  141. 141.

    Taylor-Kashton C, Lichtensztejn D, Baloglu E, Senapedis W, Shacham S, Kauffman MG, et al. XPO1 Inhibition Preferentially Disrupts the 3D Nuclear Organization of Telomeres in Tumor Cells. J Cell Physiol. 2016;231:2711–9.

  142. 142.

    Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919–22. https://doi.org/10.1038/nmeth.3999.

  143. 143.

    Fang R, Yu M, Li G, Chee S, Liu T, Schmitt AD, et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 2016;26:1345–8. https://doi.org/10.1038/cr.2016.137.

Download references

Acknowledgements

This work was supported partly by National Natural Science Foundation of China (81541153); Guangdong Provincial Science and Technology Programs (2016A050503046 and 2015A050502048); Southern Science and Engineering Guangdong Laboratory Zhanjiang (ZJW-2019-07); The Public Service Platform of South China Sea for R&D Marine Biomedicine Resources (GDMUK201808); and Research Project of “Excellent Innovative Talent Support Program” of Heilongjiang University of Traditional Chinese Medicine (2018RCD13).

Author information

Affiliations

Authors

Contributions

All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Likun Du or Xiao Zhu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent for publication

All authors consent for publication.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Tao, T., Du, L. et al. Three-dimensional genome: developmental technologies and applications in precision medicine. J Hum Genet 65, 497–511 (2020). https://doi.org/10.1038/s10038-020-0737-7

Download citation

Further reading

  • Hi-C Identifies Complex Genomic Rearrangements and TAD-Shuffling in Developmental Diseases

    • Uirá Souto Melo
    • , Robert Schöpflin
    • , Rocio Acuna-Hidalgo
    • , Martin Atta Mensah
    • , Björn Fischer-Zirnsak
    • , Manuel Holtgrewe
    • , Marius-Konstantin Klever
    • , Seval Türkmen
    • , Verena Heinrich
    • , Ilina Datkhaeva Pluym
    • , Eunice Matoso
    • , Sérgio Bernardo de Sousa
    • , Pedro Louro
    • , Wiebke Hülsemann
    • , Monika Cohen
    • , Andreas Dufke
    • , Anna Latos-Bieleńska
    • , Martin Vingron
    • , Vera Kalscheuer
    • , Fabiola Quintero-Rivera
    • , Malte Spielmann
    •  & Stefan Mundlos

    The American Journal of Human Genetics (2020)