Identification of copy number variations and translocations in cancer cells from Hi-C data

LcDel: deletion variation detection based on clustering and long reads

Motivation: Genomic structural variation refers to chromosomal level variations such as genome rearrangement or insertion/deletion, which typically involve larger DNA fragments compared to single nucleotide variations. Deletion is a common type of structural variants in the genome, which may lead to mangy diseases, so the detection of deletions can help to gain insights into the pathogenesis of diseases and provide accurate information for disease diagnosis, treatment, and prevention. Many tools exist for deletion variant detection, but they are still inadequate in some aspects, and most of them ignore the presence of chimeric variants in clustering, resulting in less precise clustering results. Results: In this paper, we present LcDel, which can detect deletion variation based on clustering and long reads. LcDel first finds the candidate deletion sites and then performs the first clustering step using two clustering methods (sliding window-based and coverage-based, respectively) based on the length of the deletion. After that, LcDel immediately uses the second clustering by hierarchical clustering to determine the location and length of the deletion. LcDel is benchmarked against some other structural variation detection tools on multiple datasets, and the results show that LcDel has better detection performance for deletion. The source code is available in https://github.com/cyq1314woaini/LcDel.

Reduced Nephron Endowment in Six2-TGCtg Mice Is Due to Six3 Misexpression by Aberrant Enhancer-Promoter Interactions in the Transgene

Key Points

Aberrant enhancer–promoter interactions detected by Hi-C drive ectopic expression of Six3 in the Six2TGCtg line. Disruption of Six3 in the Six2TGCtg line restores nephron number, implicating SIX3 interference with SIX2 function in nephron progenitor cell renewal.

Background

Lifelong kidney function relies on the complement of nephrons generated during mammalian development from a mesenchymal nephron progenitor cell population. Low nephron endowment confers increased susceptibility to CKD. Reduced nephron numbers in the popular Six2TGC transgenic mouse line may be due to disruption of a regulatory gene at the integration site and/or ectopic expression of a gene(s) contained within the transgene.

Methods

Targeted locus amplification was performed to identify the integration site of the Six2TGC transgene. Genome-wide chromatin conformation capture (Hi-C) datasets were generated from nephron progenitor cells isolated from the Six2TGC +/tg mice, the Cited1 CreERT2/+ control mice, and the Six2TGC +/tg ; Tsc1 +/Flox mice that exhibited restored nephron number compared with Six2TGC +/tg mice. Modified transgenic mice lacking the C-terminal domain of Six3 were used to evaluate the mechanism of nephron number reduction in the Six2TGC +/tg mouse line.

Results

Targeted locus amplification revealed integration of the Six2TGC transgene within an intron of Cntnap5a on chr1, and Hi-C analysis mapped the precise integration of Six2TGC and Cited1 CreERT2 transgenes to chr1 and chr14, respectively. No changes in topology, accessibility, or expression were observed within the 50-megabase region centered on Cntnap5a in Six2TGC +/tg mice compared with control mice. By contrast, we identified an aberrant regulatory interaction between a Six2 distal enhancer and the Six3 promoter contained within the transgene. Increasing the Six2TGC tg to Six2 locus ratio or removing one Six2 allele in Six2TGC +/tg mice caused severe renal hypoplasia. Furthermore, clustered regularly interspaced short palindromic repeats disruption of Six3 within the transgene (Six2TGC ∆Six3CT ) restored nephron endowment to wild-type levels and abolished the stoichiometric effect.

Conclusions

These findings broadly demonstrate the utility of Hi-C data in mapping transgene integration sites and architecture. Data from genetic and biochemical studies together suggest that in Six2TGC kidneys, SIX3 interferes with SIX2 function in nephron progenitor cell renewal through its C-terminal domain.

Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues

Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.

Cytogenetics in the management of hematological malignancies: An overview of alternative technologies for cytogenetic characterization

Genomic characterization is an essential part of the clinical management of hematological malignancies for diagnostic, prognostic and therapeutic purposes. Although CBA and FISH are still the gold standard in hematology for the detection of CNA and SV, some alternative technologies are intended to complement their deficiencies or even replace them in the more or less near future. In this article, we provide a technological overview of these alternatives. CMA is the historical and well established technique for the high-resolution detection of CNA. For SV detection, there are emerging techniques based on the study of chromatin conformation and more established ones such as RTMLPA for the detection of fusion transcripts and RNA-seq to reveal the molecular consequences of SV. Comprehensive techniques that detect both CNA and SV are the most interesting because they provide all the information in a single examination. Among these, OGM is a promising emerging higher-solution technique that offers a complete solution at a contained cost, at the expense of a relatively low throughput per machine. WGS remains the most adaptable solution, with long-read approaches enabling very high-resolution detection of CAs, but requiring a heavy bioinformatics installation and at a still high cost. However, the development of high-resolution genome-wide detection techniques for CAs allows for a much better description of chromoanagenesis. Therefore, we have included in this review an update on the various existing mechanisms and their consequences and implications, especially prognostic, in hematological malignancies.

Tracing cancer evolution and heterogeneity using Hi-C

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.

Reduced nephron endowment in the common Six2-TGC tg mouse line is due to Six3 misexpression by aberrant enhancer-promoter interactions in the transgene

Lifelong kidney function relies on the complement of nephrons generated during mammalian development from a mesenchymal nephron progenitor cell (NPC) population. Low nephron endowment confers increased susceptibility to chronic kidney disease. We asked whether reduced nephron numbers in the popular Six2TGC transgenic mouse line 1 was due to disruption of a regulatory gene at the integration site or to ectopic expression of a gene(s) contained within the transgene. Targeted locus amplification identified integration of the Six2TGC transgene within an intron of Cntnap5a on chr1. We generated Hi-C datasets from NPCs isolated from the Six2TGC tg/+ mice, the Cited1 CreERT2/+ control mice, and the Six2TGC tg/+ ; Tsc1 +/Flox,2 mice that exhibited restored nephron number compared with Six2TGC tg/+ mice, and mapped the precise integration of Six2TGC and Cited1 CreERT2 transgenes to chr1 and chr14, respectively. No changes in topology, accessibility, or expression were observed within the 50-megabase region centered on Cntnap5a in Six2TGC tg/+ mice compared with control mice. By contrast, we identified an aberrant regulatory interaction between a Six2 distal enhancer and the Six3 promoter contained within the transgene. Increasing the Six2TGC tg to Six2 locus ratio or removing one Six2 allele in Six2TGC tg/+ mice, caused severe renal hypoplasia. Furthermore, CRISPR disruption of Six3 within the transgene ( Six2TGC Δ Six3CT ) restored nephron endowment to wildtype levels and abolished the stoichiometric effect. Data from genetic and biochemical studies together suggest that in Six2TGC, SIX3 interferes with SIX2 function in NPC renewal through its C-terminal domain.

Significance

Using high-resolution chromatin conformation and accessibility datasets we mapped the integration site of two popular transgenes used in studies of nephron progenitor cells and kidney development. Aberrant enhancer-promoter interactions drive ectopic expression of Six3 in the Six2TGC tg line which was correlated with disruption of nephrogenesis. Disruption of Six3 within the transgene restored nephron numbers to control levels; further genetic and biochemical studies suggest that Six3 interferes with Six2 -mediated regulation of NPC renewal.

Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration

Persistent DNA double-strand breaks (DSBs) in neurons are an early pathological hallmark of neurodegenerative diseases including Alzheimer's disease (AD), with the potential to disrupt genome integrity. We used single-nucleus RNA-seq in human postmortem prefrontal cortex samples and found that excitatory neurons in AD were enriched for somatic mosaic gene fusions. Gene fusions were particularly enriched in excitatory neurons with DNA damage repair and senescence gene signatures. In addition, somatic genome structural variations and gene fusions were enriched in neurons burdened with DSBs in the CK-p25 mouse model of neurodegeneration. Neurons enriched for DSBs also had elevated levels of cohesin along with progressive multiscale disruption of the 3D genome organization aligned with transcriptional changes in synaptic, neuronal development, and histone genes. Overall, this study demonstrates the disruption of genome stability and the 3D genome organization by DSBs in neurons as pathological steps in the progression of neurodegenerative diseases.

EagleC Explorer: A desktop application for interactively detecting and visualizing SVs and enhancer hijacking on Hi-C contact maps

It has been shown that Hi-C can be used as a powerful tool to detect structural variations (SVs) and enhancer hijacking events. However, there has been no existing programs that can directly visualize and detect such events on a personal computer, which hinders the broad adaption of the technology for intuitive discovery in cancer studies. Here, we introduce the EagleC Explorer, a desktop software that is specifically designed for exploring Hi-C and other chromatin contact data in cancer genomes. EagleC Explorer has a set of unique features, including 1) conveniently visualizing global and local Hi-C data; 2) interactively detecting SVs on a Hi-C map for any user-selected region on screen within seconds, using a deep-learning model; 3) reconstructing local Hi-C map surrounding user-provided SVs and generating publication-quality figures; 4) detecting enhancer hijacking events for any user-suggested regions on screen. In addition, EagleC Explorer can also incorporate other genomic tracks such as RNA-Seq or ChIP-Seq to facilitate scientists for integrative data analysis and making novel discoveries.

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

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.

Cell line-specific features of 3D chromatin organization in hepatocellular carcinoma

Liver cancer, particularly hepatocellular carcinoma (HCC), poses a significant global threat to human lives. To advance the development of innovative diagnostic and treatment approaches, it is essential to examine the hidden features of HCC, particularly its 3D genome architecture, which is not well understood. In this study, we investigated the 3D genome organization of four HCC cell lines-Hep3B, Huh1, Huh7, and SNU449-using in situ Hi-C and assay for transposase-accessible chromatin sequencing. Our findings revealed that HCC cell lines had more long-range interactions, both intra-and interchromosomal, compared to human mammary epithelial cells (HMECs). Unexpectedly, HCC cell lines displayed cell line-specific compartmental modifications at the megabase (Mb) scale, which could potentially be leveraged in determining HCC subtypes. At the sub-Mb scale, we observed decreases in intra-TAD (topologically associated domain) interactions and chromatin loops in HCC cell lines compared to HMECs. Lastly, we discovered a correlation between gene expression and the 3D chromatin architecture of SLC8A1, which encodes a sodium-calcium antiporter whose modulation is known to induce apoptosis by comparison between HCC cell lines and HMECs. Our findings suggest that HCC cell lines have a distinct 3D genome organization that is different from those of normal and other cancer cells based on the analysis of compartments, TADs, and chromatin loops. Overall, we take this as evidence that genome organization plays a crucial role in cancer phenotype determination. Further exploration of epigenetics in HCC will help us to better understand specific gene regulation mechanisms and uncover novel targets for cancer treatment.

Hotspot of de novo telomere addition stabilizes linear amplicons in yeast grown in sulfate-limiting conditions

Evolution is driven by the accumulation of competing mutations that influence survival. A broad form of genetic variation is the amplification or deletion of DNA (≥50 bp) referred to as copy number variation (CNV). In humans, CNV may be inconsequential, contribute to minor phenotypic differences, or cause conditions such as birth defects, neurodevelopmental disorders, and cancers. To identify mechanisms that drive CNV, we monitored the experimental evolution of Saccharomyces cerevisiae populations grown under sulfate-limiting conditions. Cells with increased copy number of the gene SUL1, which encodes a primary sulfate transporter, exhibit a fitness advantage. Previously, we reported interstitial inverted triplications of SUL1 as the dominant rearrangement in a haploid population. Here, in a diploid population, we find instead that small linear fragments containing SUL1 form and are sustained over several generations. Many of the linear fragments are stabilized by de novo telomere addition within a telomere-like sequence near SUL1 (within the SNF5 gene). Using an assay that monitors telomerase action following an induced chromosome break, we show that this region acts as a hotspot of de novo telomere addition and that required sequences map to a region of <250 base pairs. Consistent with previous work showing that association of the telomere-binding protein Cdc13 with internal sequences stimulates telomerase recruitment, mutation of a four-nucleotide motif predicted to associate with Cdc13 abolishes de novo telomere addition. Our study suggests that internal telomere-like sequences that stimulate de novo telomere addition can contribute to adaptation by promoting genomic plasticity.

When 3D genome changes cause disease: the impact of structural variations in congenital disease and cancer

Large structural variations (SV) are a class of mutations that have long been known to cause a wide range of genetic diseases, from rare congenital disease to cancer. Many of these SVs do not directly disrupt disease-related genes and determining causal genotype-phenotype relationships has been challenging to disentangle in the past. This has started to change with our increased understanding of the 3D genome folding. The pathophysiologies of the different types of genetic diseases influence the type of SVs observed and their genetic consequences, and how these are connected to 3D genome folding. We propose guiding principles for interpreting disease-associated SVs based on our current understanding of 3D chromatin architecture and the gene-regulatory and physiological mechanisms disrupted in disease.

3D genome mapping identifies subgroup-specific chromosome conformations and tumor-dependency genes in ependymoma

Ependymoma is a tumor of the brain or spinal cord. The two most common and aggressive molecular groups of ependymoma are the supratentorial ZFTA-fusion associated and the posterior fossa ependymoma group A. In both groups, tumors occur mainly in young children and frequently recur after treatment. Although molecular mechanisms underlying these diseases have recently been uncovered, they remain difficult to target and innovative therapeutic approaches are urgently needed. Here, we use genome-wide chromosome conformation capture (Hi-C), complemented with CTCF and H3K27ac ChIP-seq, as well as gene expression and DNA methylation analysis in primary and relapsed ependymoma tumors, to identify chromosomal conformations and regulatory mechanisms associated with aberrant gene expression. In particular, we observe the formation of new topologically associating domains ('neo-TADs') caused by structural variants, group-specific 3D chromatin loops, and the replacement of CTCF insulators by DNA hyper-methylation. Through inhibition experiments, we validate that genes implicated by these 3D genome conformations are essential for the survival of patient-derived ependymoma models in a group-specific manner. Thus, this study extends our ability to reveal tumor-dependency genes by 3D genome conformations even in tumors that lack targetable genetic alterations.

Influence of human peripheral blood samples preprocessing on the quality of Hi-C libraries

The genome-wide variant of the chromatin conformation capture technique (Hi-C) is a powerful tool for revealing patterns of genome spatial organization, as well as for understanding the effects of their disturbance on disease development. In addition, Hi-C can be used to detect chromosomal rearrangements, including balanced translocations and inversions. The use of the Hi-C method for the detection of chromosomal rearrangements is becoming more widespread. Modern high-throughput methods of genome analysis can effectively reveal point mutations and unbalanced chromosomal rearrangements. However, their sensitivity for determining translocations and inversions remains rather low. The storage of whole blood samples can affect the amount and integrity of genomic DNA, and it can distort the results of subsequent analyses if the storage was not under proper conditions. The Hi-C method is extremely demanding on the input material. The necessary condition for successfully applying Hi-C and obtaining high-quality data is the preservation of the spatial chromatin organization within the nucleus. The purpose of this study was to determine the optimal storage conditions of blood samples for subsequent Hi-C analysis. We selected 10 different conditions for blood storage and sample processing. For each condition, we prepared and sequenced Hi-C libraries. The quality of the obtained data was compared. As a result of the work, we formulated the requirements for the storage and processing of samples to obtain high-quality Hi-C data. We have established the minimum volume of blood sufficient for conducting Hi-C analysis. In addition, we have identified the most suitable methods for isolation of peripheral blood mononuclear cells and their long-term storage. The main requirement we have formulated is not to freeze whole blood.

Hi-C analysis of genomic contacts revealed karyotype abnormalities in chicken HD3 cell line

BACKGROUND

Karyotype abnormalities are frequent in immortalized continuous cell lines either transformed or derived from primary tumors. Chromosomal rearrangements can cause dramatic changes in gene expression and affect cellular phenotype and behavior during in vitro culture. Structural variations of chromosomes in many continuous mammalian cell lines are well documented, but chromosome aberrations in cell lines from other vertebrate models often remain understudied. The chicken LSCC-HD3 cell line (HD3), generated from erythroid precursors, was used as an avian model for erythroid differentiation and lineage-specific gene expression. However, karyotype abnormalities in the HD3 cell line were not assessed. In the present study, we applied high-throughput chromosome conformation capture to analyze 3D genome organization and to detect chromosome rearrangements in the HD3 cell line.

RESULTS

We obtained Hi-C maps of genomic interactions for the HD3 cell line and compared A/B compartments and topologically associating domains between HD3 and several other cell types. By analysis of contact patterns in the Hi-C maps of HD3 cells, we identified more than 25 interchromosomal translocations of regions ≥ 200 kb on both micro- and macrochromosomes. We classified most of the observed translocations as unbalanced, leading to the formation of heteromorphic chromosomes. In many cases of microchromosome rearrangements, an entire microchromosome together with other macro- and microchromosomes participated in the emergence of a derivative chromosome, resembling "chromosomal fusions'' between acrocentric microchromosomes. Intrachromosomal inversions, deletions and duplications were also detected in HD3 cells. Several of the identified simple and complex chromosomal rearrangements, such as between GGA2 and GGA1qter; GGA5, GGA4p and GGA7p; GGA4q, GGA6 and GGA19; and duplication of the sex chromosome GGAW, were confirmed by FISH.

CONCLUSIONS

In the erythroid progenitor HD3 cell line, in contrast to mature and immature erythrocytes, the genome is organized into distinct topologically associating domains. The HD3 cell line has a severely rearranged karyotype with most of the chromosomes engaged in translocations and can be used in studies of genome structure-function relationships. Hi-C proved to be a reliable tool for simultaneous assessment of the spatial genome organization and chromosomal aberrations in karyotypes of birds with a large number of microchromosomes.

HiSV: A control-free method for structural variation detection from Hi-C data

Structural variations (SVs) play an essential role in the evolution of human genomes and are associated with cancer genetics and rare disease. High-throughput chromosome capture (Hi-C) technology probed all genome-wide crosslinked chromatin to study the spatial architecture of chromosomes. Hi-C read pairs can span megabases, making the technology useful for detecting large-scale SVs. So far, the identification of SVs from Hi-C data is still in the early stages with only a few methods available. Especially, no algorithm has been developed that can detect SVs without control samples. Therefore, we developed HiSV (Hi-C for Structural Variation), a control-free method for identifying large-scale SVs from a Hi-C sample. Inspired by the single image saliency detection model, HiSV constructed a saliency map of interaction frequencies and extracted saliency segments as large-scale SVs. By evaluating both simulated and real data, HiSV not only detected all variant types, but also achieved a higher level of accuracy and sensitivity than existing methods. Moreover, our results on cancer cell lines showed that HiSV effectively detected eight complex SV events and identified two novel SVs of key factors associated with cancer development. Finally, we found that integrating the result of HiSV helped the WGS method to identify a total number of 94 novel SVs in two cancer cell lines.

Hi-C Analysis to Identify Genome-Wide Chromatin Structural Aberration in Cancer

Hi-C is a method that analyzes genome-wide chromatin structure using next-generation sequencer. Chromatin structure is crucial for regulating transcription or replication, and Hi-C has revealed the hierarchical chromatin structures, such as loop, domain , and compartment structures. Aberrant alteration of these structures causes disease, and a number of structural aberrations in cancer cells have been reported recently. Besides, Hi-C can identify chromosome rearrangements that frequently occurred in cancer. Therefore, Hi-C is a powerful technique to analyze epigenomic and genomic aberrations in tumorigenesis. Here we will introduce the basic protocol of Hi-C in experimental and analytical aspects.

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C

BACKGROUND

Regulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells.

RESULTS

Compared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter-enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops.

CONCLUSION

We determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

Insights into the cell fate decision-making processes from chromosome structural reorganizations

The cell fate decision-making process, which provides the capability of a cell transition to a new cell type, involves the reorganizations of 3D genome structures. Currently, the high temporal resolution picture of how the chromosome structural rearrangements occur and further influence the gene activities during the cell-state transition is still challenging to acquire. Here, we study the chromosome structural reorganizations during the cell-state transitions among the pluripotent embryonic stem cell, the terminally differentiated normal cell, and the cancer cell using a nonequilibrium landscape-switching model implemented in the molecular dynamics simulation. We quantify the chromosome (de)compaction pathways during the cell-state transitions and find that the two pathways having the same destinations can merge prior to reaching the final states. The chromosomes at the merging states have similar structural geometries but can differ in long-range compartment segregation and spatial distribution of the chromosomal loci and genes, leading to cell-type-specific transition mechanisms. We identify the irreversible pathways of chromosome structural rearrangements during the forward and reverse transitions connecting the same pair of cell states, underscoring the critical roles of nonequilibrium dynamics in the cell-state transitions. Our results contribute to the understanding of the cell fate decision-making processes from the chromosome structural perspective.

Translocation detection from Hi-C data via scan statistics

Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the inter-chromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in inter-chromosomal translocation detection using Hi-C data. This article is protected by copyright. All rights reserved.

Chromosomal Translocations Detection in Cancer Cells Using Chromosomal Conformation Capture Data

Complex chromosomal rearrangements such as translocations play a critical role in oncogenesis. Translocation detection is vital to decipher their biological role in activating cancer-associated mechanisms. High-throughput chromosomal conformations capture (Hi-C) data have shown promising progress in unveiling the genome variations in a disease condition. Until now, multiple structural data (Hi-C)-based methods are available that can detect translocations in cancer genomes. However, the consistency and specificity of Hi-C-based translocation results still need to be validated with conventional methods. This study used Hi-C data of cancerous cell lines, namely lung cancer (A549), Chronic Myelogenous Leukemia (K562), and Acute Monocytic Leukemia (THP-1), to detect the translocations. The results were cross-validated through whole-genome sequencing (WGS) and paired-read analysis. Moreover, PCR amplification validated the presence of translocated reads in different chromosomes. By integrating different data types, we showed that the results of Hi-C data are as reliable as WGS and can be utilized as an assistive method for detecting translocations in the diseased genome. Our findings support the utility of Hi-C technology to detect the translocations and study their effects on the three-dimensional architecture of the genome in cancer condition.

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

Alterations in transcriptional networks in cancer: the role of noncoding somatic driver mutations

Aberrant gene expression is a cancer hallmark and it is known that almost every tumor acquires somatic mutations in transcription factors, chromatin regulators, or the DNA regulatory elements that are critical for transcriptional control and cell phenotype. While the role of transcription factors and chromatin regulators has been widely studied, relatively few noncoding driver mutations have been identified and functionally characterized to date. Here, we review the current understanding of somatic variants in noncoding regions of the cancer genome and their impact on chromatin architecture and transcriptional networks. We also discuss approaches and ongoing challenges for noncoding driver discovery, and highlight insights gained from recent studies exploring the nature and impact of noncoding drivers on tumor formation.

Comparative characterization of 3D chromatin organization in triple-negative breast cancers

Triple-negative breast cancer (TNBC) is a malignant cancer subtype with a high risk of recurrence and an aggressive phenotype compared to other breast cancer subtypes. Although many breast cancer studies conducted to date have investigated genetic variations and differential target gene expression, how 3D chromatin architectures are reorganized in TNBC has been poorly elucidated. Here, using in situ Hi-C technology, we characterized the 3D chromatin organization in cells representing five distinct subtypes of breast cancer (including TNBC) compared to that in normal cells. We found that the global and local 3D architectures were severely disrupted in breast cancer. TNBC cell lines (especially BT549 cells) showed the most dramatic changes relative to normal cells. Importantly, we detected CTCF-dependent TNBC-susceptible losses/gains of 3D chromatin organization and found that these changes were strongly associated with perturbed chromatin accessibility and transcriptional dysregulation. In TNBC tissue, 3D chromatin disorganization was also observed relative to the 3D chromatin organization in normal tissues. We observed that the perturbed local 3D architectures found in TNBC cells were partially conserved in TNBC tissues. Finally, we discovered distinct tissue-specific chromatin loops by comparing normal and TNBC tissues. In this study, we elucidated the characteristics of the 3D chromatin organization in breast cancer relative to normal cells/tissues at multiple scales and identified associations between disrupted structures and various epigenetic features and transcriptomes. Collectively, our findings reveal important 3D chromatin structural features for future diagnostic and therapeutic studies of TNBC.

The 3D architecture of the genome is dramatically altered in an aggressive form of breast cancer, leading to changes in the regulation of gene expression that can fuel tumor growth. A team from South Korea, led by Hyeong-Gon Moon of Seoul National University College of Medicine and Daeyoup Lee of the Korea Advanced Institute of Science and Technology, Daejeon, detailed how chromosomes are positioned and folded within the nucleus of cell liness from five different subtypes of breast cancer. They found that triple-negative breast cancers displayed the most extreme reorganization of their genomes, a pattern also observed in biopsy tissues taken from patients with this subtype of cancer. Knowledge of these conformational changes could inform future efforts to develop therapies and diagnostics for patients with triple-negative breast tumors.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

HolistIC: leveraging Hi-C and whole genome shotgun sequencing for double minute chromosome discovery

MOTIVATION

Double minute (DM) chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug-resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of DM can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor DMs. This creates DMs with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing (WGS) in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates.

RESULTS

In this study, we describe an algorithm called 'HolistIC' that can predict DMs in tumor genomes by integrating WGS and Hi-C sequencing data. The consolidation of these sources of information resolves ambiguity in DM amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi-C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC's ability to predict DMs from Hi-C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish DMs that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation.

AVAILABILITY AND IMPLEMENTATION

Our software, named 'HolistIC', is available at http://www.github.com/mhayes20/HolistIC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of Hi-C Data for Discovery of Structural Variations in Cancer

Structural variations (SVs) are large genomic rearrangements that can be challenging to identify with current short read sequencing technology due to various confounding factors such as existence of genomic repeats and complex SV structures. Hi-C breakfinder is the first computational tool that utilizes the technology of high-throughput chromatin conformation capture assay (Hi-C) to systematically identify SVs, without being interfered by regular confounding factors. SVs change the spatial distance of genomic regions and cause discontinuous signals in Hi-C, which are difficult to analyze by routine informatics practice. Here we provide step-by-step guidance for how to identify SVs using Hi-C data and how to reconstruct Hi-C maps in the presence of SVs.

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.

Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines

BACKGROUND

The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data.

RESULTS

We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets.

CONCLUSIONS

AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra.

Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.

Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.

Spatial genome architecture and the emergence of malignancy

Human chromosomes are large spatially and hierarchically structured entities, the integrity of which needs to be preserved throughout the lifespan of the cell and in conjunction with cell cycle progression. Preservation of chromosomal structure is important for proper deployment of cell type-specific gene expression programs. Thus, aberrations in the integrity and structure of chromosomes will predictably lead to disease, including cancer. Here, we provide an updated standpoint with respect to chromatin misfolding and the emergence of various cancer types. We discuss recent studies implicating the disruption of topologically associating domains, switching between active and inactive compartments, rewiring of promoter-enhancer interactions in malignancy as well as the effects of single nucleotide polymorphisms in non-coding regions involved in long-range regulatory interactions. In light of these findings, we argue that chromosome conformation studies may now also be useful for patient diagnosis and drug target discovery.

Structural Variations of the 3D Genome Architecture in Cervical Cancer Development

Human papillomavirus (HPV) integration is the major contributor to cervical cancer (CC) development by inducing structural variations (SVs) in the human genome. SVs are directly associated with the three-dimensional (3D) genome structure leading to cancer development. The detection of SVs is not a trivial task, and several genome-wide techniques have greatly helped in the identification of SVs in the cancerous genome. However, in cervical cancer, precise prediction of SVs mainly translocations and their effects on 3D-genome and gene expression still need to be explored. Here, we have used high-throughput chromosome conformation capture (Hi-C) data of cervical cancer to detect the SVs, especially the translocations, and validated it through whole-genome sequencing (WGS) data. We found that the cervical cancer 3D-genome architecture rearranges itself as compared to that in the normal tissue, and 24% of the total genome switches their A/B compartments. Moreover, translocation detection from Hi-C data showed the presence of high-resolution t(4;7) (q13.1; q31.32) and t(1;16) (q21.2; q22.1) translocations, which disrupted the expression of the genes located at and nearby positions. Enrichment analysis suggested that the disrupted genes were mainly involved in controlling cervical cancer-related pathways. In summary, we detect the novel SVs through Hi-C data and unfold the association among genome-reorganization, translocations, and gene expression regulation. The results help understand the underlying pathogenicity mechanism of SVs in cervical cancer development and identify the targeted therapeutics against cervical cancer.

Genome reconstruction and haplotype phasing using chromosome conformation capture methodologies

Genomic analysis of individuals or organisms is predicated on the availability of high-quality reference and genotype information. With the rapidly dropping costs of high-throughput DNA sequencing, this is becoming readily available for diverse organisms and for increasingly large populations of individuals. Despite these advances, there are still aspects of genome sequencing that remain challenging for existing sequencing methods. This includes the generation of long-range contiguity during genome assembly, identification of structural variants in both germline and somatic tissues, the phasing of haplotypes in diploid organisms and the resolution of genome sequence for organisms derived from complex samples. These types of information are valuable for understanding the role of genome sequence and genetic variation on genome function, and numerous approaches have been developed to address them. Recently, chromosome conformation capture (3C) experiments, such as the Hi-C assay, have emerged as powerful tools to aid in these challenges for genome reconstruction. We will review the current use of Hi-C as a tool for aiding in genome sequencing, addressing the applications, strengths, limitations and potential future directions for the use of 3C data in genome analysis. We argue that unique features of Hi-C experiments make this data type a powerful tool to address challenges in genome sequencing, and that future integration of Hi-C data with alternative sequencing assays will facilitate the continuing revolution in genomic analysis and genome sequencing.

Integrative Analysis of Genome, 3D Genome, and Transcriptome Alterations of Clinical Lung Cancer Samples

Genomic studies of cancer cell alterations, such as mutations, copy number variations (CNVs), and translocations, greatly promote our understanding of the genesis and development of cancer. However, the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties. To explore the 3D genome structure in clinical lung cancer, we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer, combining with RNA-seq analysis. We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells (1 × 10⁴), compared the genome architecture between clinical samples and cell lines of lung cancer, and identified conserved and changed spatial chromatin structures between normal and cancer samples. We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer. By integrating those different types of cancer alterations, we showed significant associations between CNVs, 3D genome, and gene expression. We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures. Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.

HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data

Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples.

AVAILABILITY AND IMPLEMENTATION

HiTea is available at https://github.com/parklab/HiTea and as a Docker image.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Robust detection of translocations in lymphoma FFPE samples using targeted locus capture-based sequencing

In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens.

Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes

Recent efforts have shown that structural variations (SVs) can disrupt three-dimensional genome organization and induce enhancer hijacking, yet no computational tools exist to identify such events from chromatin interaction data. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, including interchromosomal translocations, large deletions and inversions. Our framework can automatically resolve complex SVs, reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects and predict chromatin loops induced by SVs. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer hijacking. To experimentally validate NeoLoopFinder, we deleted the hijacked enhancers in prostate adenocarcinoma cells using CRISPR-Cas9, which significantly reduced expression of the target oncogene. In summary, NeoLoopFinder enables identification of critical oncogenic regulatory elements that can potentially reveal therapeutic targets.

Hi-C as a molecular rangefinder to examine genomic rearrangements

The mammalian genome is highly packed into the nucleus. Over the past decade, the development of Hi-C has contributed significantly to our understanding of the three-dimensional (3D) chromatin structure, uncovering the principles and functions of higher-order chromatin organizations. Recent studies have repositioned its property in spatial proximity measurement to address challenging problems in genome analyses including genome assembly, haplotype phasing, and the detection of genomic rearrangements. In particular, the power of Hi-C in detecting large-scale structural variations (SVs) in the cancer genome has been demonstrated, which is challenging to be addressed solely with short-read-based whole-genome sequencing analyses. In this review, we first provide a comprehensive view of Hi-C as an intuitive and effective SV detection tool. Then, we introduce recently developed bioinformatics tools utilizing Hi-C to investigate genomic rearrangements. Finally, we discuss the potential application of single-cell Hi-C to address the heterogeneity of genomic rearrangements and sub-population identification in the cancer genome.

Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines

BACKGROUND

Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined.

RESULTS

We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality.

CONCLUSIONS

Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

A cookbook for DNase Hi-C

Background

The Hi-C technique is widely employed to study the 3-dimensional chromatin architecture and to assemble genomes. The conventional in situ Hi-C protocol employs restriction enzymes to digest chromatin, which results in nonuniform genomic coverage. Using sequence-agnostic restriction enzymes, such as DNAse I, could help to overcome this limitation.

Results

In this study, we compare different DNAse Hi-C protocols and identify the critical steps that significantly affect the efficiency of the protocol. In particular, we show that the SDS quenching strategy strongly affects subsequent chromatin digestion. The presence of biotinylated oligonucleotide adapters may lead to ligase reaction by-products, which can be avoided by rational design of the adapter sequences. Moreover, the use of nucleotide-exchange enzymes for biotin fill-in enables simultaneous labelling and repair of DNA ends, similar to the conventional Hi-C protocol. These improvements simplify the protocol, making it less expensive and time-consuming.

Conclusions

We propose a new robust protocol for the preparation of DNAse Hi-C libraries from cultured human cells and blood samples supplemented with experimental controls and computational tools for the evaluation of library quality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-021-00389-5.

Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines

Background

Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map.

Results

We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel ‘entire-fragment’ counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD’s explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias.

Conclusions

HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.. [DOI: 10.1101/2020.10.06.328567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

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

Genome-wide analysis methods, such as array comparative genomic hybridization (CGH) and whole-genome sequencing (WGS), have greatly advanced the identification of structural variants (SVs) in the human genome. However, even with standard high-throughput sequencing techniques, complex rearrangements with multiple breakpoints are often difficult to resolve, and predicting their effects on gene expression and phenotype remains a challenge. Here, we address these problems by using high-throughput chromosome conformation capture (Hi-C) generated from cultured cells of nine individuals with developmental disorders (DDs). Three individuals had previously been identified as harboring duplications at the SOX9 locus and six had been identified with translocations. Hi-C resolved the positions of the duplications and was instructive in interpreting their distinct pathogenic effects, including the formation of new topologically associating domains (neo-TADs). Hi-C was very sensitive in detecting translocations, and it revealed previously unrecognized complex rearrangements at the breakpoints. In several cases, we observed the formation of fused-TADs promoting ectopic enhancer-promoter interactions that were likely to be involved in the disease pathology. In summary, we show that Hi-C is a sensible method for the detection of complex SVs in a clinical setting. The results help interpret the possible pathogenic effects of the SVs in individuals with DDs.

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.

The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

Structural variation in the sequencing era

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

The 3D Genome as a Target for Anticancer Therapy

The role of 3D genome organization in the precise regulation of gene expression is well established. Accordingly, the mechanistic connections between 3D genome alterations and disease development are becoming increasingly apparent. This opinion article provides a snapshot of our current understanding of the 3D genome alterations associated with cancers. We discuss potential connections of the 3D genome and cancer transcriptional addiction phenomenon as well as molecular mechanisms of action of 3D genome-disrupting drugs. Finally, we highlight issues and perspectives raised by the discovery of the first pharmaceutical strongly affecting 3D genome organization.

Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2

Fit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify significant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship between genomic distance and contact probability without any parametric assumption. The spline fit together with the correction of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb-2 Mb for human genome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform genome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to significant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between convergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory). FitHiC2 is available through Bioconda, GitHub and the Python Package Index.

Structural variant calling: the long and the short of it

Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.