1
|
Nammo T, Funahashi N, Udagawa H, Kozawa J, Nakano K, Shimizu Y, Okamura T, Kawaguchi M, Uebanso T, Nishimura W, Hiramoto M, Shimomura I, Yasuda K. Single-housing-induced islet epigenomic changes are related to polymorphisms in diabetic KK mice. Life Sci Alliance 2024; 7:e202302099. [PMID: 38876803 PMCID: PMC11178941 DOI: 10.26508/lsa.202302099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/16/2024] Open
Abstract
A lack of social relationships is increasingly recognized as a type 2 diabetes (T2D) risk. To investigate the underlying mechanism, we used male KK mice, an inbred strain with spontaneous diabetes. Given the association between living alone and T2D risk in humans, we divided the non-diabetic mice into singly housed (KK-SH) and group-housed control mice. Around the onset of diabetes in KK-SH mice, we compared H3K27ac ChIP-Seq with RNA-Seq using pancreatic islets derived from each experimental group, revealing a positive correlation between single-housing-induced changes in H3K27ac and gene expression levels. In particular, single-housing-induced H3K27ac decreases revealed a significant association with islet cell functions and GWAS loci for T2D and related diseases, with significant enrichment of binding motifs for transcription factors representative of human diabetes. Although these H3K27ac regions were preferentially localized to a polymorphic genomic background, SNVs and indels did not cause sequence disruption of enriched transcription factor motifs in most of these elements. These results suggest alternative roles of genetic variants in environment-dependent epigenomic changes and provide insights into the complex mode of disease inheritance.
Collapse
Affiliation(s)
- Takao Nammo
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Diabetes Care Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Nobuaki Funahashi
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Haruhide Udagawa
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Registered Dietitians, Faculty of Health and Nutrition, Bunkyo University, Chigasaki, Japan
| | - Junji Kozawa
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Diabetes Care Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kenta Nakano
- https://ror.org/00r9w3j27 Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Yukiko Shimizu
- https://ror.org/00r9w3j27 Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Tadashi Okamura
- https://ror.org/00r9w3j27 Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Miho Kawaguchi
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Takashi Uebanso
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Preventive Environment and Nutrition, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Wataru Nishimura
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Molecular Biology, International University of Health and Welfare School of Medicine, Chiba, Japan
- Division of Anatomy, Bio-Imaging and Neuro-cell Science, Jichi Medical University, Tochigi, Japan
| | - Masaki Hiramoto
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Biochemistry, Tokyo Medical University, Tokyo, Japan
| | - Iichiro Shimomura
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kazuki Yasuda
- https://ror.org/00r9w3j27 Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Diabetes, Endocrinology and Metabolism, Kyorin University School of Medicine, Tokyo, Japan
| |
Collapse
|
2
|
Cao C, Xu Q, Zhu Z, Xu M, Wei Y, Lin S, Cheng S, Zhi W, Hong P, Huang X, Lin D, Cao G, Meng Y, Wu P, Peng T, Wei J, Ding W, Huang X, Sung W, Chen G, Ma D, Li G, Wu P. Three-dimensional chromatin analysis reveals Sp1 as a mediator to program and reprogram HPV-host epigenetic architecture in cervical cancer. Cancer Lett 2024; 588:216809. [PMID: 38471646 DOI: 10.1016/j.canlet.2024.216809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
Human papillomavirus (HPV) is predominantly associated with HPV-related cancers, however, the precise mechanisms underlying the HPV-host epigenetic architectures in HPV carcinogenesis remain elusive. Here, we employed high-throughput chromosome conformation capture (Hi-C) to comprehensively map HPV16/18-host chromatin interactions. Our study identified the transcription factor Sp1 as a pivotal mediator in programming HPV-host interactions. By targeting Sp1, the active histone modifications (H3K27ac, H3K4me1, and H3K4me3) and the HPV-host chromatin interactions are reprogrammed, which leads to the downregulation of oncogenes located near the integration sites in both HPV (E6/E7) and the host genome (KLF5/MYC). Additionally, Sp1 inhibition led to the upregulation of immune checkpoint genes by reprogramming histone modifications in host cells. Notably, humanized patient-derived xenograft (PDX-HuHSC-NSG) models demonstrated that Sp1 inhibition promoted anti-PD-1 immunotherapy via remodeling the tumor immune microenvironment in cervical cancer. Moreover, single-cell transcriptomic analysis validated the enrichment of transcription factor Sp1 in epithelial cells of cervical cancer. In summary, our findings elucidate Sp1 as a key mediator involved in the programming and reprogramming of HPV-host epigenetic architecture. Inhibiting Sp1 with plicamycin may represent a promising therapeutic option for HPV-related carcinoma.
Collapse
Affiliation(s)
- Canhui Cao
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Xu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Zhixian Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Miaochun Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ye Wei
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shitong Lin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Cheng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Wenhua Zhi
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Hong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xingyu Huang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Da Lin
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China; State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
| | - Gang Cao
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China; State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
| | - Yifan Meng
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Wu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Peng
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juncheng Wei
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wencheng Ding
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyuan Huang
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - WingKin Sung
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China; School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore
| | - Gang Chen
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ding Ma
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan, China.
| | - Peng Wu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| |
Collapse
|
3
|
Han MH, Park J, Park M. Advances in the multimodal analysis of the 3D chromatin structure and gene regulation. Exp Mol Med 2024; 56:763-771. [PMID: 38658704 PMCID: PMC11059362 DOI: 10.1038/s12276-024-01246-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024] Open
Abstract
Recent studies have demonstrated that the three-dimensional conformation of the chromatin plays a crucial role in gene regulation, with aberrations potentially leading to various diseases. Advanced methodologies have revealed a link between the chromatin conformation and biological function. This review divides these methodologies into sequencing-based and imaging-based methodologies, tracing their development over time. We particularly highlight innovative techniques that facilitate the simultaneous mapping of RNAs, histone modifications, and proteins within the context of the 3D architecture of chromatin. This multimodal integration substantially improves our ability to establish a robust connection between the spatial arrangement of molecular components in the nucleus and their functional roles. Achieving a comprehensive understanding of gene regulation requires capturing diverse data modalities within individual cells, enabling the direct inference of functional relationships between these components. In this context, imaging-based technologies have emerged as an especially promising approach for gathering spatial information across multiple components in the same cell.
Collapse
Affiliation(s)
- Man-Hyuk Han
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jihyun Park
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Minhee Park
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- KAIST Stem Cell Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
| |
Collapse
|
4
|
Yoon I, Kim U, Song Y, Park T, Lee DS. 3C methods in cancer research: recent advances and future prospects. Exp Mol Med 2024; 56:788-798. [PMID: 38658701 PMCID: PMC11059347 DOI: 10.1038/s12276-024-01236-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/15/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
In recent years, Hi-C technology has revolutionized cancer research by elucidating the mystery of three-dimensional chromatin organization and its role in gene regulation. This paper explored the impact of Hi-C advancements on cancer research by delving into high-resolution techniques, such as chromatin loops, structural variants, haplotype phasing, and extrachromosomal DNA (ecDNA). Distant regulatory elements interact with their target genes through chromatin loops. Structural variants contribute to the development and progression of cancer. Haplotype phasing is crucial for understanding allele-specific genomic rearrangements and somatic clonal evolution in cancer. The role of ecDNA in driving oncogene amplification and drug resistance in cancer cells has also been revealed. These innovations offer a deeper understanding of cancer biology and the potential for personalized therapies. Despite these advancements, challenges, such as the accurate mapping of repetitive sequences and precise identification of structural variants, persist. Integrating Hi-C with multiomics data is key to overcoming these challenges and comprehensively understanding complex cancer genomes. Thus, Hi-C is a powerful tool for guiding precision medicine in cancer research and treatment.
Collapse
Affiliation(s)
- Insoo Yoon
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Uijin Kim
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Yousuk Song
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Taesoo Park
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea
| | - Dong-Sung Lee
- Department of Life Science, University of Seoul, Seoul, 02504, Republic of Korea.
| |
Collapse
|
5
|
Ren L, Ma W, Wang Y. SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation. Comput Biol Med 2024; 171:108182. [PMID: 38422958 DOI: 10.1016/j.compbiomed.2024.108182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/18/2024] [Accepted: 02/18/2024] [Indexed: 03/02/2024]
Abstract
Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.
Collapse
Affiliation(s)
- Lixin Ren
- Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, 100083, Beijing, China.
| | - Wanbiao Ma
- Department of Applied Mathematics, School of Mathematics and Physics, University of Science and Technology Beijing, 100083, Beijing, China.
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 330106, China.
| |
Collapse
|
6
|
Nolan B, Harris HL, Kalluchi A, Reznicek TE, Cummings CT, Rowley MJ. HiCrayon reveals distinct layers of multi-state 3D chromatin organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.11.579821. [PMID: 38405883 PMCID: PMC10888951 DOI: 10.1101/2024.02.11.579821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The co-visualization of chromatin conformation with 1D 'omics data is key to the multi-omics driven data analysis of 3D genome organization. Chromatin contact maps are often shown as 2D heatmaps and visually compared to 1D genomic data by simple juxtaposition. While common, this strategy is imprecise, placing the onus on the reader to align features with each other. To remedy this, we developed HiCrayon, an interactive tool that facilitates the integration of 3D chromatin organization maps and 1D datasets. This visualization method integrates data from genomic assays directly into the chromatin contact map by coloring interactions according to 1D signal. HiCrayon is implemented using R shiny and python to create a graphical user interface (GUI) application, available in both web or containerized format to promote accessibility. HiCrayon is implemented in R, and includes a graphical user interface (GUI), as well as a slimmed-down web-based version that lets users quickly produce publication-ready images. We demonstrate the utility of HiCrayon in visualizing the effectiveness of compartment calling and the relationship between ChIP-seq and various features of chromatin organization. We also demonstrate the improved visualization of other 3D genomic phenomena, such as differences between loops associated with CTCF/cohesin vs. those associated with H3K27ac. We then demonstrate HiCrayon's visualization of organizational changes that occur during differentiation and use HiCrayon to detect compartment patterns that cannot be assigned to either A or B compartments, revealing a distinct 3rd chromatin compartment. Overall, we demonstrate the utility of co-visualizing 2D chromatin conformation with 1D genomic signals within the same matrix to reveal fundamental aspects of genome organization. Local version: https://github.com/JRowleyLab/HiCrayon Web version: https://jrowleylab.com/HiCrayon.
Collapse
Affiliation(s)
- Ben Nolan
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| | - Hannah L Harris
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| | - Achyuth Kalluchi
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| | - Timothy E Reznicek
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| | - Christopher T Cummings
- Department of Pediatrics, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| | - M Jordan Rowley
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Emile St, Omaha, 68198, NE, USA
| |
Collapse
|
7
|
Lou S, Lin S. An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods. PLoS One 2024; 19:e0287521. [PMID: 38232107 PMCID: PMC10793909 DOI: 10.1371/journal.pone.0287521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/07/2023] [Indexed: 01/19/2024] Open
Abstract
The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.
Collapse
Affiliation(s)
- Shuyuan Lou
- Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH, United States of America
| | - Shili Lin
- Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH, United States of America
- Department of Statistics, The Ohio State University, Columbus, OH, United States of America
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH, United States of America
| |
Collapse
|
8
|
Umarov R, Hon CC. Enhancer target prediction: state-of-the-art approaches and future prospects. Biochem Soc Trans 2023; 51:1975-1988. [PMID: 37830459 DOI: 10.1042/bst20230917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023]
Abstract
Enhancers are genomic regions that regulate gene transcription and are located far away from the transcription start sites of their target genes. Enhancers are highly enriched in disease-associated variants and thus deciphering the interactions between enhancers and genes is crucial to understanding the molecular basis of genetic predispositions to diseases. Experimental validations of enhancer targets can be laborious. Computational methods have thus emerged as a valuable alternative for studying enhancer-gene interactions. A variety of computational methods have been developed to predict enhancer targets by incorporating genomic features (e.g. conservation, distance, and sequence), epigenomic features (e.g. histone marks and chromatin contacts) and activity measurements (e.g. covariations of enhancer activity and gene expression). With the recent advances in genome perturbation and chromatin conformation capture technologies, data on experimentally validated enhancer targets are becoming available for supervised training of these methods and evaluation of their performance. In this review, we categorize enhancer target prediction methods based on their rationales and approaches. Then we discuss their merits and limitations and highlight the future directions for enhancer targets prediction.
Collapse
Affiliation(s)
- Ramzan Umarov
- RIKEN Centre for Integrative Medical Sciences, Yokohama RIKEN Institute, Yokohama, Japan
| | - Chung-Chau Hon
- RIKEN Centre for Integrative Medical Sciences, Yokohama RIKEN Institute, Yokohama, Japan
| |
Collapse
|
9
|
Xu J, Zhang P, Sun W, Zhang J, Zhang W, Hou C, Li L. EpiMCI: Predicting Multi-Way Chromatin Interactions from Epigenomic Signals. BIOLOGY 2023; 12:1203. [PMID: 37759602 PMCID: PMC10525350 DOI: 10.3390/biology12091203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/31/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023]
Abstract
The recently emerging high-throughput Pore-C (HiPore-C) can identify whole-genome high-order chromatin multi-way interactions with an ultra-high output, contributing to deciphering three-dimensional (3D) genome organization. However, it also brings new challenges to relevant data analysis. To alleviate this problem, we proposed the EpiMCI, a model for multi-way chromatin interaction prediction based on a hypergraph neural network with epigenomic signals as the input. The EpiMCI integrated separate hyperedge representations with coupling hyperedge information and obtained AUCs of 0.981 and 0.984 in the GM12878 and K562 datasets, respectively, which outperformed the current available method. Moreover, the EpiMCI can be applied to denoise the HiPore-C data and improve the data quality efficiently. Furthermore, the vertex embeddings extracted from the EpiMCI reflected the global chromatin architecture accurately. The principal component analysis suggested that it was well aligned with the activities of genomic regions at the chromatin compartment level. Taken together, the EpiMCI can accurately predict multi-way chromatin interactions and can be applied to studies relying on chromatin architecture.
Collapse
Affiliation(s)
- Jinsheng Xu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Ping Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weicheng Sun
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Junying Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wenxue Zhang
- Food Science Program, Division of Food, Nutrition and Exercise Sciences, University of Missouri, 1406 E Rollins Street, Columbia, MO 65211, USA
| | - Chunhui Hou
- China State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Li Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430074, China
| |
Collapse
|
10
|
Raj S, Sifuentes CJ, Kyono Y, Denver RJ. Metamorphic gene regulation programs in Xenopus tropicalis tadpole brain. PLoS One 2023; 18:e0287858. [PMID: 37384728 PMCID: PMC10310023 DOI: 10.1371/journal.pone.0287858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/14/2023] [Indexed: 07/01/2023] Open
Abstract
Amphibian metamorphosis is controlled by thyroid hormone (TH), which binds TH receptors (TRs) to regulate gene expression programs that underlie morphogenesis. Gene expression screens using tissues from premetamorphic tadpoles treated with TH identified some TH target genes, but few studies have analyzed genome-wide changes in gene regulation during spontaneous metamorphosis. We analyzed RNA sequencing data at four developmental stages from the beginning to the end of spontaneous metamorphosis, conducted on the neuroendocrine centers of Xenopus tropicalis tadpole brain. We also conducted chromatin immunoprecipitation sequencing (ChIP-seq) for TRs, and we compared gene expression changes during metamorphosis with those induced by exogenous TH. The mRNA levels of 26% of protein coding genes changed during metamorphosis; about half were upregulated and half downregulated. Twenty four percent of genes whose mRNA levels changed during metamorphosis had TR ChIP-seq peaks. Genes involved with neural cell differentiation, cell physiology, synaptogenesis and cell-cell signaling were upregulated, while genes involved with cell cycle, protein synthesis, and neural stem/progenitor cell homeostasis were downregulated. There is a shift from building neural structures early in the metamorphic process, to the differentiation and maturation of neural cells and neural signaling pathways characteristic of the adult frog brain. Only half of the genes modulated by treatment of premetamorphic tadpoles with TH for 16 h changed expression during metamorphosis; these represented 33% of the genes whose mRNA levels changed during metamorphosis. Taken together, our results provide a foundation for understanding the molecular basis for metamorphosis of tadpole brain, and they highlight potential caveats for interpreting gene regulation changes in premetamorphic tadpoles induced by exogenous TH.
Collapse
Affiliation(s)
- Samhitha Raj
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Christopher J. Sifuentes
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yasuhiro Kyono
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Robert J. Denver
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States of America
| |
Collapse
|
11
|
Chan B, Rubinstein M. Theory of chromatin organization maintained by active loop extrusion. Proc Natl Acad Sci U S A 2023; 120:e2222078120. [PMID: 37253009 PMCID: PMC10266055 DOI: 10.1073/pnas.2222078120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 04/13/2023] [Indexed: 06/01/2023] Open
Abstract
The active loop extrusion hypothesis proposes that chromatin threads through the cohesin protein complex into progressively larger loops until reaching specific boundary elements. We build upon this hypothesis and develop an analytical theory for active loop extrusion which predicts that loop formation probability is a nonmonotonic function of loop length and describes chromatin contact probabilities. We validate our model with Monte Carlo and hybrid Molecular Dynamics-Monte Carlo simulations and demonstrate that our theory recapitulates experimental chromatin conformation capture data. Our results support active loop extrusion as a mechanism for chromatin organization and provide an analytical description of chromatin organization that may be used to specifically modify chromatin contact probabilities.
Collapse
Affiliation(s)
- Brian Chan
- Department of Biomedical Engineering, Duke University, Durham, NC27708
| | - Michael Rubinstein
- Department of Biomedical Engineering, Duke University, Durham, NC27708
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC27708
- Department of Chemistry, Duke University, Durham, NC27708
- Department of Physics, Duke University, Durham, NC27708
- Institute for Chemical Reaction Design and Discovery (World Premier International Research Center Initiative-ICReDD), Hokkaido University, Sapporo001-0021, Japan
| |
Collapse
|
12
|
Qiu Y, Feng D, Jiang W, Zhang T, Lu Q, Zhao M. 3D genome organization and epigenetic regulation in autoimmune diseases. Front Immunol 2023; 14:1196123. [PMID: 37346038 PMCID: PMC10279977 DOI: 10.3389/fimmu.2023.1196123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.
Collapse
Affiliation(s)
- Yueqi Qiu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Delong Feng
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenjuan Jiang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Tingting Zhang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- State Key Laboratory of Natural Medicines, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ming Zhao
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| |
Collapse
|
13
|
Gong H, Li M, Ji M, Zhang X, Yuan Z, Zhang S, Yang Y, Li C, Chen Y. MINE is a method for detecting spatial density of regulatory chromatin interactions based on a multi-modal network. CELL REPORTS METHODS 2023; 3:100386. [PMID: 36814847 PMCID: PMC9939382 DOI: 10.1016/j.crmeth.2022.100386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/15/2022] [Accepted: 12/16/2022] [Indexed: 06/18/2023]
Abstract
Chromatin interactions play essential roles in chromatin conformation and gene expression. However, few tools exist to analyze the spatial density of regulatory chromatin interactions (SD-RCI). Here, we present the multi-modal network (MINE) toolkit, including MINE-Loop, MINE-Density, and MINE-Viewer. The MINE-Loop network aims to enhance the detection of RCIs, MINE-Density quantifies the SD--RCI, and MINE-Viewer facilitates 3D visualization of the density of chromatin interactions and participating regulatory factors (e.g., transcription factors). We applied MINE to investigate the relationship between the SD-RCI and chromatin volume change in HeLa cells before and after liquid-liquid phase separation. Changes in SD-RCI before and after treating the HeLa cells with 1,6-hexanediol suggest that changes in chromatin organization was related to the degree of activation or repression of genes. Together, the MINE toolkit enables quantitative studies on different aspects of chromatin conformation and regulatory activity.
Collapse
Affiliation(s)
- Haiyan Gong
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Mengdie Ji
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Xiaotong Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
| | - Zan Yuan
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Sichen Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yi Yang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Chun Li
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yang Chen
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| |
Collapse
|
14
|
Аpplication of massive parallel reporter analysis in biotechnology and medicine. КЛИНИЧЕСКАЯ ПРАКТИКА 2023. [DOI: 10.17816/clinpract115063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The development and functioning of an organism relies on tissue-specific gene programs. Genome regulatory elements play a key role in the regulation of such programs, and disruptions in their function can lead to the development of various pathologies, including cancers, malformations and autoimmune diseases. The emergence of high-throughput genomic studies has led to massively parallel reporter analysis (MPRA) methods, which allow the functional verification and identification of regulatory elements on a genome-wide scale. Initially MPRA was used as a tool to investigate fundamental aspects of epigenetics, but the approach also has great potential for clinical and practical biotechnology. Currently, MPRA is used for validation of clinically significant mutations, identification of tissue-specific regulatory elements, search for the most promising loci for transgene integration, and is an indispensable tool for creating highly efficient expression systems, the range of application of which extends from approaches for protein development and design of next-generation therapeutic antibody superproducers to gene therapy. In this review, the main principles and areas of practical application of high-throughput reporter assays will be discussed.
Collapse
|
15
|
Gupta K, Wang G, Zhang S, Gao X, Zheng R, Zhang Y, Meng Q, Zhang L, Cao Q, Chen K. StripeDiff: Model-based algorithm for differential analysis of chromatin stripe. SCIENCE ADVANCES 2022; 8:eabk2246. [PMID: 36475785 PMCID: PMC9728969 DOI: 10.1126/sciadv.abk2246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/28/2022] [Indexed: 05/27/2023]
Abstract
Multiple recent studies revealed stripes as an architectural feature of three-dimensional chromatin and found stripes connected to epigenetic regulation of transcription. Whereas a couple of tools are available to define stripes in a single sample, there is yet no reported method to quantitatively measure the dynamic change of each stripe between samples. Here, we developed StripeDiff, a bioinformatics tool that delivers a set of statistical methods to detect differential stripes between samples. StripeDiff showed optimal performance in both simulation data analysis and real Hi-C data analysis. Applying StripeDiff to 12 sets of Hi-C data revealed new insights into the connection between change of chromatin stripe and change of chromatin modification, transcriptional regulation, and cell differentiation. StripeDiff will be a robust tool for the community to facilitate understanding of stripes and their function in numerous biological models.
Collapse
Affiliation(s)
- Krishan Gupta
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Guangyu Wang
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Houston Methodist Hospital Research Institute, Houston, TX 77030, USA
| | - Shuo Zhang
- Houston Methodist Hospital Research Institute, Houston, TX 77030, USA
| | - Xinlei Gao
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Rongbin Zheng
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Yanchun Zhang
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Qingshu Meng
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lili Zhang
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Houston Methodist Hospital Research Institute, Houston, TX 77030, USA
| | - Qi Cao
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Kaifu Chen
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Houston Methodist Hospital Research Institute, Houston, TX 77030, USA
- Broad Institute of MIT and Harvard, Boston, MA 02115, USA
- Dana-Farber/Harvard Cancer Center, Boston, MA 02115, USA
| |
Collapse
|
16
|
Kanezaki R, Toki T, Terui K, Sato T, Kobayashi A, Kudo K, Kamio T, Sasaki S, Kawaguchi K, Watanabe K, Ito E. Mechanism of KIT gene regulation by GATA1 lacking the N-terminal domain in Down syndrome-related myeloid disorders. Sci Rep 2022; 12:20587. [PMID: 36447001 PMCID: PMC9708825 DOI: 10.1038/s41598-022-25046-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022] Open
Abstract
Children with Down syndrome (DS) are at high risk of transient abnormal myelopoiesis (TAM) and myeloid leukemia of DS (ML-DS). GATA1 mutations are detected in almost all TAM and ML-DS samples, with exclusive expression of short GATA1 protein (GATA1s) lacking the N-terminal domain (NTD). However, it remains to be clarified how GATA1s is involved with both disorders. Here, we established the K562 GATA1s (K562-G1s) clones expressing only GATA1s by CRISPR/Cas9 genome editing. The K562-G1s clones expressed KIT at significantly higher levels compared to the wild type of K562 (K562-WT). Chromatin immunoprecipitation studies identified the GATA1-bound regulatory sites upstream of KIT in K562-WT, K562-G1s clones and two ML-DS cell lines; KPAM1 and CMK11-5. Sonication-based chromosome conformation capture (3C) assay demonstrated that in K562-WT, the - 87 kb enhancer region of KIT was proximal to the - 115 kb, - 109 kb and + 1 kb region, while in a K562-G1s clone, CMK11-5 and primary TAM cells, the - 87 kb region was more proximal to the KIT transcriptional start site. These results suggest that the NTD of GATA1 is essential for proper genomic conformation and regulation of KIT gene expression, and that perturbation of this function might be involved in the pathogenesis of TAM and ML-DS.
Collapse
Affiliation(s)
- Rika Kanezaki
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Tsutomu Toki
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Kiminori Terui
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Tomohiko Sato
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Akie Kobayashi
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Ko Kudo
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Takuya Kamio
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Shinya Sasaki
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan
| | - Koji Kawaguchi
- grid.415798.60000 0004 0378 1551Department of Hematology and Oncology, Shizuoka Children’s Hospital, Shizuoka, Japan
| | - Kenichiro Watanabe
- grid.415798.60000 0004 0378 1551Department of Hematology and Oncology, Shizuoka Children’s Hospital, Shizuoka, Japan
| | - Etsuro Ito
- grid.257016.70000 0001 0673 6172Department of Pediatrics, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562 Japan ,grid.257016.70000 0001 0673 6172Department of Community Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| |
Collapse
|
17
|
Yang M, Ma J. Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization. J Mol Biol 2022; 434:167666. [PMID: 35659533 DOI: 10.1016/j.jmb.2022.167666] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 01/25/2023]
Abstract
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. However, DNA sequence determinants that modulate the formation of 3D genome organization remain poorly characterized. In the past few years, predicting 3D genome organization based on DNA sequence features has become an active area of research. Here, we review the recent progress in computational approaches to unraveling important sequence elements for 3D genome organization. In particular, we discuss the rapid development of machine learning-based methods that facilitate the connections between DNA sequence features and 3D genome architectures at different scales. While much progress has been made in developing predictive models for revealing important sequence features for 3D genome organization, new research is urgently needed to incorporate multi-omic data and enhance model interpretability, further advancing our understanding of gene regulation mechanisms through the lens of 3D genome organization.
Collapse
Affiliation(s)
- Muyu Yang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States. https://twitter.com/muyu_wendy_yang
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States.
| |
Collapse
|
18
|
Kang Y, Jung WJ, Brent MR. Predicting which genes will respond to transcription factor perturbations. G3 (BETHESDA, MD.) 2022; 12:jkac144. [PMID: 35666184 PMCID: PMC9339286 DOI: 10.1093/g3journal/jkac144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022]
Abstract
The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.
Collapse
Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Wooseok J Jung
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| |
Collapse
|
19
|
Fan Y, Peng B. StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning. BMC Bioinformatics 2022; 23:272. [PMID: 35820811 PMCID: PMC9277947 DOI: 10.1186/s12859-022-04821-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background Understanding the regulatory role of enhancer–promoter interactions (EPIs) on specific gene expression in cells contributes to the understanding of gene regulation, cell differentiation, etc., and its identification has been a challenging task. On the one hand, using traditional wet experimental methods to identify EPIs often means a lot of human labor and time costs. On the other hand, although the currently proposed computational methods have good recognition effects, they generally require a long training time. Results In this study, we studied the EPIs of six human cell lines and designed a cell line-specific EPIs prediction method based on a stacking ensemble learning strategy, which has better prediction performance and faster training speed, called StackEPI. Specifically, by combining different encoding schemes and machine learning methods, our prediction method can extract the cell line-specific effective information of enhancer and promoter gene sequences comprehensively and in many directions, and make accurate recognition of cell line-specific EPIs. Ultimately, the source code to implement StackEPI and experimental data involved in the experiment are available at https://github.com/20032303092/StackEPI.git. Conclusions The comparison results show that our model can deliver better performance on the problem of identifying cell line-specific EPIs and outperform other state-of-the-art models. In addition, our model also has a more efficient computation speed. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04821-9.
Collapse
Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Binchao Peng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| |
Collapse
|
20
|
Mora A, Huang X, Jauhari S, Jiang Q, Li X. Chromatin Hubs: A biological and computational outlook. Comput Struct Biotechnol J 2022; 20:3796-3813. [PMID: 35891791 PMCID: PMC9304431 DOI: 10.1016/j.csbj.2022.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/02/2022] [Accepted: 07/02/2022] [Indexed: 11/20/2022] Open
Abstract
This review discusses our current understanding of chromatin biology and bioinformatics under the unifying concept of “chromatin hubs.” The first part reviews the biology of chromatin hubs, including chromatin–chromatin interaction hubs, chromatin hubs at the nuclear periphery, hubs around macromolecules such as RNA polymerase or lncRNAs, and hubs around nuclear bodies such as the nucleolus or nuclear speckles. The second part reviews existing computational methods, including enhancer–promoter interaction prediction, network analysis, chromatin domain callers, transcription factory predictors, and multi-way interaction analysis. We introduce an integrated model that makes sense of the existing evidence. Understanding chromatin hubs may allow us (i) to explain long-unsolved biological questions such as interaction specificity and redundancy of mechanisms, (ii) to develop more realistic kinetic and functional predictions, and (iii) to explain the etiology of genomic disease.
Collapse
Affiliation(s)
- Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, PR China
- Corresponding authors.
| | - Xiaowei Huang
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, PR China
| | - Shaurya Jauhari
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, PR China
| | - Qin Jiang
- Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210000, PR China
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, and Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, PR China
- Corresponding authors.
| |
Collapse
|
21
|
Piecyk RS, Schlegel L, Johannes F. Predicting 3D chromatin interactions from DNA sequence using Deep Learning. Comput Struct Biotechnol J 2022; 20:3439-3448. [PMID: 35832620 PMCID: PMC9271978 DOI: 10.1016/j.csbj.2022.06.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/22/2022] Open
Abstract
Gene regulation in eukaryotes is profoundly shaped by the 3D organization of chromatin within the cell nucleus. Distal regulatory interactions between enhancers and their target genes are widespread and many causal loci underlying heritable agricultural or clinical traits have been mapped to distal cis-regulatory elements. Dissecting the sequence features that mediate such distal interactions is key to understanding their underlying biology. Deep Learning (DL) models coupled with genome-wide 3C-based sequencing data have emerged as powerful tools to infer the DNA sequence grammar underlying such distal interactions. In this review we show that most DL models have remarkably high prediction accuracy, which indicates that DNA sequence features are important determinants of chromatin looping. However, DL model training has so far been limited to a small set of human cell lines, raising questions about the generalization of these predictions to other tissue-types and species. Furthermore, we find that the model architecture seems less relevant for model performance than the training strategy and the data preparation step. Transfer learning, coupled with functionally curated interactions, appear to be the most promising approach to learn cell-type specific and possibly species- specific sequence features in future applications.
Collapse
Affiliation(s)
- Robert S. Piecyk
- Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany
| | - Luca Schlegel
- Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany
| | - Frank Johannes
- Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany
- TUM Institute for Advanced Study, Garching, Germany
| |
Collapse
|
22
|
Functional annotation of breast cancer risk loci: current progress and future directions. Br J Cancer 2022; 126:981-993. [PMID: 34741135 PMCID: PMC8980003 DOI: 10.1038/s41416-021-01612-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/20/2022] Open
Abstract
Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.
Collapse
|
23
|
InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification. Genes (Basel) 2022; 13:genes13040621. [PMID: 35456427 PMCID: PMC9026820 DOI: 10.3390/genes13040621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 02/01/2023] Open
Abstract
Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease.
Collapse
|
24
|
Wei W, Zhao Q, Wang Z, Liau WS, Basic D, Ren H, Marshall PR, Zajaczkowski EL, Leighton LJ, Madugalle SU, Musgrove M, Periyakaruppiah A, Shi J, Zhang J, Mattick JS, Mercer TR, Spitale RC, Li X, Bredy TW. ADRAM is an experience-dependent long noncoding RNA that drives fear extinction through a direct interaction with the chaperone protein 14-3-3. Cell Rep 2022; 38:110546. [PMID: 35320727 PMCID: PMC9015815 DOI: 10.1016/j.celrep.2022.110546] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/03/2022] [Accepted: 02/28/2022] [Indexed: 11/25/2022] Open
Abstract
Here, we used RNA capture-seq to identify a large population of lncRNAs that are expressed in the infralimbic prefrontal cortex of adult male mice in response to fear-related learning. Combining these data with cell-type-specific ATAC-seq on neurons that had been selectively activated by fear extinction learning, we find inducible 434 lncRNAs that are derived from enhancer regions in the vicinity of protein-coding genes. In particular, we discover an experience-induced lncRNA we call ADRAM (activity-dependent lncRNA associated with memory) that acts as both a scaffold and a combinatorial guide to recruit the brain-enriched chaperone protein 14-3-3 to the promoter of the memory-associated immediate-early gene Nr4a2 and is required fear extinction memory. This study expands the lexicon of experience-dependent lncRNA activity in the brain and highlights enhancer-derived RNAs (eRNAs) as key players in the epigenomic regulation of gene expression associated with the formation of fear extinction memory.
Collapse
Affiliation(s)
- Wei Wei
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China; Brain Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China; Medical Research Institute, Wuhan University, Wuhan, China.
| | - Qiongyi Zhao
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Ziqi Wang
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Wei-Siang Liau
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Dean Basic
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Haobin Ren
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Paul R Marshall
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Esmi L Zajaczkowski
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Laura J Leighton
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Sachithrani U Madugalle
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Mason Musgrove
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Ambika Periyakaruppiah
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Jichun Shi
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China; Brain Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jianjian Zhang
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - John S Mattick
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, Australia
| | - Timothy R Mercer
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Robert C Spitale
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, USA
| | - Xiang Li
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China; Brain Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China; Medical Research Institute, Wuhan University, Wuhan, China
| | - Timothy W Bredy
- Cognitive Neuroepigenetics Laboratory, Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
| |
Collapse
|
25
|
Abstract
We found the three-dimensional (3D) structure of chromatin at the chromosome level to be highly conserved for more than 50 million y of carnivore evolution. Intrachromosomal contacts were maintained even after chromosome rearrangements within carnivore lineages, demonstrating that the maintenance of 3D chromatin architecture is essential for conserved genome functions. These discoveries enabled the identification of orthologous chromosomal DNA segments among related species, a method we call 3D comparative scaffotyping. The method has application for putative chromosome assignment of chromosome-scale DNA sequence scaffolds produced by de novo genome sequencing. Broadly applied to biodiversity genome sequencing efforts, the approach can reduce costs associated with karyotyping and the physical mapping of DNA segments to chromosomes. High throughput chromatin conformation capture (Hi-C) of leukocyte DNA was used to investigate the evolutionary stability of chromatin conformation at the chromosomal level in 11 species from three carnivore families: Felidae, Canidae, and Ursidae. Chromosome-scale scaffolds (C-scaffolds) of each species were initially used for whole-genome alignment to a reference genome within each family. This approach established putative orthologous relationships between C-scaffolds among the different species. Hi-C contact maps for all C-scaffolds were then visually compared and found to be distinct for a given reference chromosome or C-scaffold within a species and indistinguishable for orthologous C-scaffolds having a 1:1 relationship within a family. The visual patterns within families were strongly supported by eigenvectors from the Hi-C contact maps. Analysis of Hi-C contact maps and eigenvectors across the three carnivore families revealed that most cross-family orthologous subchromosomal fragments have a conserved three-dimensional (3D) chromatin structure and thus have been under strong evolutionary constraint for ∼54 My of carnivore evolution. The most pronounced differences in chromatin conformation were observed for the X chromosome and the red fox genome, whose chromosomes have undergone extensive rearrangements relative to other canids. We also demonstrate that Hi-C contact map pattern analysis can be used to accurately identify orthologous relationships between C-scaffolds and chromosomes, a method we termed “3D comparative scaffotyping.” This method provides a powerful means for estimating karyotypes in de novo sequenced species that have unknown karyotype and no physical mapping information.
Collapse
|
26
|
Hait TA, Elkon R, Shamir R. CT-FOCS: a novel method for inferring cell type-specific enhancer–promoter maps. Nucleic Acids Res 2022; 50:e55. [PMID: 35100425 PMCID: PMC9178001 DOI: 10.1093/nar/gkac048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/09/2022] [Accepted: 01/15/2022] [Indexed: 11/13/2022] Open
Abstract
Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer–promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS (cell type FOCS), a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.
Collapse
Affiliation(s)
- Tom Aharon Hait
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| |
Collapse
|
27
|
Hammelman J, Krismer K, Gifford DK. spatzie: an R package for identifying significant transcription factor motif co-enrichment from enhancer–promoter interactions. Nucleic Acids Res 2022; 50:e52. [PMID: 35100401 PMCID: PMC9122533 DOI: 10.1093/nar/gkac036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 01/07/2022] [Accepted: 01/29/2022] [Indexed: 01/30/2023] Open
Abstract
Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer–promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer–promoter interaction data. The method is available online at https://spatzie.mit.edu.
Collapse
Affiliation(s)
- Jennifer Hammelman
- Computational and Systems Biology Program, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
| | - Konstantin Krismer
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - David K Gifford
- Computational and Systems Biology Program, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| |
Collapse
|
28
|
Yadav VK, Singh S, Yadav A, Agarwal N, Singh B, Jalmi SK, Yadav VK, Tiwari VK, Kumar V, Singh R, Sawant SV. Stress Conditions Modulate the Chromatin Interactions Network in Arabidopsis. Front Genet 2022; 12:799805. [PMID: 35069698 PMCID: PMC8766718 DOI: 10.3389/fgene.2021.799805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/15/2021] [Indexed: 11/26/2022] Open
Abstract
Stresses have been known to cause various responses like cellular physiology, gene regulation, and genome remodeling in the organism to cope and survive. Here, we assessed the impact of stress conditions on the chromatin-interactome network of Arabidopsis thaliana. We identified thousands of chromatin interactions in native as well as in salicylic acid treatment and high temperature conditions in a genome-wide fashion. Our analysis revealed the definite pattern of chromatin interactions and stress conditions could modulate the dynamics of chromatin interactions. We found the heterochromatic region of the genome actively involved in the chromatin interactions. We further observed that the establishment or loss of interactions in response to stress does not result in the global change in the expression profile of interacting genes; however, interacting regions (genes) containing motifs for known TFs showed either lower expression or no difference than non-interacting genes. The present study also revealed that interactions preferred among the same epigenetic state (ES) suggest interactions clustered the same ES together in the 3D space of the nucleus. Our analysis showed that stress conditions affect the dynamics of chromatin interactions among the chromatin loci and these interaction networks govern the folding principle of chromatin by bringing together similar epigenetic marks.
Collapse
Affiliation(s)
- Vikash Kumar Yadav
- CSIR-National Botanical Research Institute, Lucknow, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Swadha Singh
- CSIR-National Botanical Research Institute, Lucknow, India.,School of Natural Sciences, University of California, Merced, Merced, CA, United States
| | - Amrita Yadav
- CSIR-National Botanical Research Institute, Lucknow, India
| | - Neha Agarwal
- CSIR-National Botanical Research Institute, Lucknow, India
| | - Babita Singh
- CSIR-National Botanical Research Institute, Lucknow, India
| | | | | | - Vipin Kumar Tiwari
- CSIR-National Botanical Research Institute, Lucknow, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Verandra Kumar
- Department of Botany, Manyawar Kanshiram Government Degree College, Aligarh, India
| | | | - Samir Vishwanath Sawant
- CSIR-National Botanical Research Institute, Lucknow, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| |
Collapse
|
29
|
Leveraging cell-type-specific regulatory networks to interpret genetic variants in abdominal aortic aneurysm. Proc Natl Acad Sci U S A 2022; 119:2115601119. [PMID: 34930827 PMCID: PMC8740683 DOI: 10.1073/pnas.2115601119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2021] [Indexed: 12/17/2022] Open
Abstract
Abdominal aortic aneurysm (AAA) is a common and severe disease with major genetic risk factors. In this study we generated enhancer-promoter contact data to identify regulatory elements in AAA-relevant cell types and identified changes in their predicted chromatin accessibility between AAA patients and controls. We integrated this information with disease-associated variants in regulatory elements and gene bodies to further understand the etiology and pathogenetic mechanisms of AAA. Our study combined whole-genome sequencing data with gene regulatory relations in disease-relevant cell types to reveal the important roles of the interleukin 6 pathway and ERG and KLF regulation in AAA pathogenesis. Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting disease-associated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants.
Collapse
|
30
|
Dobson T, Swaminathan J. Chromatin Immunoprecipitation Assays on Medulloblastoma Cell Line DAOY. Methods Mol Biol 2022; 2423:39-50. [PMID: 34978686 DOI: 10.1007/978-1-0716-1952-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Studies of DNA-protein interactions have revealed regulatory mechanisms of DNA replication, repair, remodeling, and transcription. Perturbation of any or all of these processes result in differential gene expression that can lead to tumor development. Chromatin immunoprecipitation assay (ChIP), currently the only method available to explore DNA-binding in vivo, has become a vastly utilized tool for cancer research. In this article we discuss an assay specified for a pediatric medulloblastoma (MB) cell line DAOY used to determine binding of transcription factors, to detect histone modifications, and to identify novel therapeutic targets.
Collapse
Affiliation(s)
- Tara Dobson
- Department of Pediatrics, UT MD Anderson Cancer Center, Houston, TX, USA
| | | |
Collapse
|
31
|
Romanov SE, Kalashnikova DA, Laktionov PP. Methods of massive parallel reporter assays for investigation of enhancers. Vavilovskii Zhurnal Genet Selektsii 2021; 25:344-355. [PMID: 34901731 PMCID: PMC8627875 DOI: 10.18699/vj21.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 03/28/2021] [Accepted: 03/28/2021] [Indexed: 11/19/2022] Open
Abstract
The correct deployment of genetic programs for development and differentiation relies on finely coordinated regulation of specific gene sets. Genomic regulatory elements play an exceptional role in this process. There are few types of gene regulatory elements, including promoters, enhancers, insulators and silencers. Alterations of gene regulatory elements may cause various pathologies, including cancer, congenital disorders and autoimmune diseases. The development of high-throughput genomic assays has made it possible to significantly accelerate the accumulation of information about the characteristic epigenetic properties of regulatory elements. In combination with high-throughput studies focused on the genome-wide distribution of epigenetic marks, regulatory proteins and the spatial structure of chromatin, this significantly expands the understanding of the principles of epigenetic regulation of genes and allows potential regulatory elements to be searched for in silico. However, common experimental approaches used to study the local characteristics of chromatin have a number of technical limitations that may reduce the reliability of computational identification of genomic regulatory sequences. Taking into account the variability of the functions of epigenetic determinants and complex multicomponent regulation of genomic elements activity, their functional verification is often required. A plethora of methods have been developed to study the functional role of regulatory elements on the genome scale. Common experimental approaches for in silico identification of regulatory elements and their inherent technical limitations will be described. The present review is focused on original high-throughput methods of enhancer activity reporter analysis that are currently used to validate predicted regulatory elements and to perform de novo searches. The methods described allow assessing the functional role of the nucleotide sequence of a regulatory element, to determine its exact boundaries and to assess the influence of the local state of chromatin on the activity of enhancers and gene expression. These approaches have contributed substantially to the understanding of the fundamental principles of gene regulation.
Collapse
Affiliation(s)
- S E Romanov
- Novosibirsk State University, Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk, Russia Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Genomics Laboratory, Novosibirsk, Russia
| | - D A Kalashnikova
- Novosibirsk State University, Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk, Russia Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Genomics Laboratory, Novosibirsk, Russia
| | - P P Laktionov
- Novosibirsk State University, Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk, Russia Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Genomics Laboratory, Novosibirsk, Russia
| |
Collapse
|
32
|
Goel VY, Hansen AS. The macro and micro of chromosome conformation capture. WILEY INTERDISCIPLINARY REVIEWS. DEVELOPMENTAL BIOLOGY 2021; 10:e395. [PMID: 32987449 PMCID: PMC8236208 DOI: 10.1002/wdev.395] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/21/2020] [Accepted: 07/31/2020] [Indexed: 12/14/2022]
Abstract
The 3D organization of the genome facilitates gene regulation, replication, and repair, making it a key feature of genomic function and one that remains to be properly understood. Over the past two decades, a variety of chromosome conformation capture (3C) methods have delineated genome folding from megabase-scale compartments and topologically associating domains (TADs) down to kilobase-scale enhancer-promoter interactions. Understanding the functional role of each layer of genome organization is a gateway to understanding cell state, development, and disease. Here, we discuss the evolution of 3C-based technologies for mapping 3D genome organization. We focus on genomics methods and provide a historical account of the development from 3C to Hi-C. We also discuss ChIP-based techniques that focus on 3D genome organization mediated by specific proteins, capture-based methods that focus on particular regions or regulatory elements, 3C-orthogonal methods that do not rely on restriction digestion and proximity ligation, and methods for mapping the DNA-RNA and RNA-RNA interactomes. We consider the biological discoveries that have come from these methods, examine the mechanistic contributions of CTCF, cohesin, and loop extrusion to genomic folding, and detail the 3D genome field's current understanding of nuclear architecture. Finally, we give special consideration to Micro-C as an emerging frontier in chromosome conformation capture and discuss recent Micro-C findings uncovering fine-scale chromatin organization in unprecedented detail. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics.
Collapse
Affiliation(s)
- Viraat Y. Goel
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Anders S. Hansen
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| |
Collapse
|
33
|
Schmidt F, Marx A, Baumgarten N, Hebel M, Wegner M, Kaulich M, Leisegang M, Brandes R, Göke J, Vreeken J, Schulz M. Integrative analysis of epigenetics data identifies gene-specific regulatory elements. Nucleic Acids Res 2021; 49:10397-10418. [PMID: 34508352 PMCID: PMC8501997 DOI: 10.1093/nar/gkab798] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 08/01/2021] [Accepted: 09/07/2021] [Indexed: 12/19/2022] Open
Abstract
Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.
Collapse
Affiliation(s)
- Florian Schmidt
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore, Singapore
| | - Alexander Marx
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- International Max Planck Research School for Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
| | - Marie Hebel
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
| | - Martin Wegner
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
| | - Manuel Kaulich
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, 60590 Frankfurt am Main, Germany
| | - Matthias S Leisegang
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
- Institute for Cardiovascular Physiology, Goethe University, 60590 Frankfurt am Main, Germany
| | - Ralf P Brandes
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
- Institute for Cardiovascular Physiology, Goethe University, 60590 Frankfurt am Main, Germany
| | - Jonathan Göke
- Laboratory of Computational Transcriptomics, Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore, Singapore
| | - Jilles Vreeken
- CISPA Helmholtz Center for Information Security, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
| |
Collapse
|
34
|
Wang H, Huang B, Wang J. Predict long-range enhancer regulation based on protein-protein interactions between transcription factors. Nucleic Acids Res 2021; 49:10347-10368. [PMID: 34570239 PMCID: PMC8501976 DOI: 10.1093/nar/gkab841] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 08/10/2021] [Accepted: 09/10/2021] [Indexed: 12/18/2022] Open
Abstract
Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.
Collapse
Affiliation(s)
- Hao Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA
| | - Binbin Huang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA
| |
Collapse
|
35
|
CoolBox: a flexible toolkit for visual analysis of genomics data. BMC Bioinformatics 2021; 22:489. [PMID: 34629071 PMCID: PMC8504052 DOI: 10.1186/s12859-021-04408-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/27/2021] [Indexed: 01/20/2023] Open
Abstract
Background Data visualization, especially the genome track plots, is crucial for genomics researchers to discover patterns in large-scale sequencing dataset. Although existing tools works well for producing a normal view of the input data, they are not convenient when users want to create customized data representations. Such gap between the visualization and data processing, prevents the users to uncover more hidden structure of the dataset. Results We developed CoolBox—an open-source toolkit for visual analysis of genomics data. This user-friendly toolkit is highly compatible with the Python ecosystem and customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife. For example, to produce high-quality genome track plots or fetch commonly used genomic data files with a Python script or command line, to explore genomic data interactively within Jupyter environment or web browser. Moreover, owing to the highly extensible Application Programming Interface design, users can customize their own tracks without difficulty, which greatly facilitate analytical, comparative genomic data visualization tasks. Conclusions CoolBox allows users to produce high-quality visualization plots and explore their data in a flexible, programmable and user-friendly way.
Collapse
|
36
|
Ryu JK, Hwang DE, Choi JM. Current Understanding of Molecular Phase Separation in Chromosomes. Int J Mol Sci 2021; 22:10736. [PMID: 34639077 PMCID: PMC8509192 DOI: 10.3390/ijms221910736] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/12/2022] Open
Abstract
Biomolecular phase separation denotes the demixing of a specific set of intracellular components without membrane encapsulation. Recent studies have found that biomolecular phase separation is involved in a wide range of cellular processes. In particular, phase separation is involved in the formation and regulation of chromosome structures at various levels. Here, we review the current understanding of biomolecular phase separation related to chromosomes. First, we discuss the fundamental principles of phase separation and introduce several examples of nuclear/chromosomal biomolecular assemblies formed by phase separation. We also briefly explain the experimental and computational methods used to study phase separation in chromosomes. Finally, we discuss a recent phase separation model, termed bridging-induced phase separation (BIPS), which can explain the formation of local chromosome structures.
Collapse
Affiliation(s)
- Je-Kyung Ryu
- Department of Biological Sciences, KAIST, Daejeon 34141, Korea
| | - Da-Eun Hwang
- Department of Chemistry, Pusan National University, Busan 46241, Korea;
| | - Jeong-Mo Choi
- Department of Chemistry, Pusan National University, Busan 46241, Korea;
| |
Collapse
|
37
|
Peres LC, Monteiro AN. Scratching Below the Ovarian Cancer GWAS Surface. Cancer Epidemiol Biomarkers Prev 2021; 30:1604-1606. [PMID: 34475121 DOI: 10.1158/1055-9965.epi-21-0568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/12/2021] [Accepted: 06/11/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent notable treatment advancements, ovarian cancer survival rates remain poor, with about half of women surviving five years after diagnosis. Uncovering novel prognostic factors is critical to better understand and reduce mortality from this deadly disease. While genome-wide association studies have identified numerous loci associated with risk of epithelial ovarian cancer, the investigation of genetic factors associated with outcomes among women with ovarian cancer has been limited due to several challenges summarized in the present commentary. Using data from the Ovarian Cancer Association Consortium, Quinn and colleagues conducted a genome-wide association study of patients with ovarian cancer receiving debulking surgery and standard chemotherapy as first-line treatment, revealing a locus at 12q24.33 associated with progression-free survival. Experimental evidence suggests that ULK1, a gene coding for a serine/threonine kinase implicated in autophagy, is the target of the association. We discuss the novelty of these findings, unanswered questions, and next steps for the road ahead in translating the work of Quinn and colleagues into clinical practice.See related article by Quinn et al., p. 1669.
Collapse
Affiliation(s)
- Lauren C Peres
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Alvaro N Monteiro
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
| |
Collapse
|
38
|
Laser Capture Microdissection of Single Neurons with Morphological Visualization Using Fluorescent Proteins Fused to Transmembrane Proteins. eNeuro 2021; 8:ENEURO.0275-20.2021. [PMID: 34400471 PMCID: PMC8422851 DOI: 10.1523/eneuro.0275-20.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/12/2021] [Accepted: 07/15/2021] [Indexed: 11/23/2022] Open
Abstract
Gene expression analysis in individual neuronal types helps in understanding brain function. Genetic methods expressing fluorescent proteins are widely used to label specific neuronal populations. However, because cell type specificity of genetic labeling is often limited, it is advantageous to combine genetic labeling with additional methods to select specific cell/neuronal types. Laser capture microdissection is one of such techniques with which one can select a specific cell/neuronal population based on morphological observation. However, a major issue is the disappearance of fluorescence signals during the tissue processing that is required for high-quality sample preparation. Here, we developed a simple, novel method in which fluorescence signals are preserved. We use genetic labeling with fluorescence proteins fused to transmembrane proteins, which shows highly stable fluorescence retention and allows for the selection of fluorescent neurons/cells based on morphology. Using this method in mice, we laser-captured neuronal somata and successfully isolated RNA. We determined that ∼100 cells are sufficient to obtain a sample required for downstream applications such as quantitative PCR. Capability to specifically microdissect targeted neurons was demonstrated by an ∼10-fold increase in mRNA for fluorescent proteins in visually identified neurons expressing the fluorescent proteins compared with neighboring cells not expressing it. We applied this method to validate virus-mediated single-cell knockout, which showed up to 92% reduction in knocked-out gene RNA compared with wild-type neurons. This method using fluorescent proteins fused to transmembrane proteins provides a new, simple solution to perform gene expression analysis in sparsely labeled neuronal/cellular populations, which is especially advantageous when genetic labeling has limited specificity.
Collapse
|
39
|
Luo Y, Vlaeminck-Guillem V, Baron S, Dallel S, Zhang CX, Le Romancer M. MEN1 silencing aggravates tumorigenic potential of AR-independent prostate cancer cells through nuclear translocation and activation of JunD and β-catenin. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:270. [PMID: 34446068 PMCID: PMC8393735 DOI: 10.1186/s13046-021-02058-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022]
Abstract
Background Recent studies highlighted the increased frequency of AR-low or -negative prostate cancers (PCas) and the importance of AR-independent mechanisms in driving metastatic castration-resistant PCa (mCRPC) development and progression. Several previous studies have highlighted the involvement of the MEN1 gene in PCa. In the current study, we focused on its role specifically in AR-independent PCa cells. Methods Cell tumorigenic features were evaluated by proliferation assay, foci formation, colony formation in soft agar, wound healing assay and xenograft experiments in mice. Quantitative RT-PCR, Western blot and immunostaining were performed to determine the expression of different factors in human PCa lines. Different ChIP-qPCR-based assays were carried out to dissect the action of JunD and β-catenin. Results We found that MEN1 silencing in AR-independent cell lines, DU145 and PC3, resulted in an increase in anchorage independence and cell migration, accompanied by sustained MYC expression. By searching for factors known to positively regulate MYC expression and play a relevant role in PCa development and progression, we uncovered that MEN1-KD triggered the nuclear translocation of JunD and β-catenin. ChIP and 3C analyses further demonstrated that MEN1-KD led to, on the one hand, augmented binding of JunD to the MYC 5′ enhancer and increased formation of loop structure, and on the other hand, increased binding of β-catenin to the MYC promoter. Moreover, the expression of several molecular markers of EMT, including E-cadherin, BMI1, Twist1 and HIF-1α, was altered in MEN1-KD DU145 and PC3 cells. In addition, analyses using cultured cells and PC3-GFP xenografts in mice demonstrated that JunD and β-catenin are necessary for the altered tumorigenic potential triggered by MEN1 inactivation in AR-independent PCa cells. Finally, we observed a significant negative clinical correlation between MEN1 and CTNNB1 mRNA expression in primary PCa and mCRPC datasets. Conclusions Our current work highlights an unrecognized oncosuppressive role for menin specifically in AR-independent PCa cells, through the activation of JunD and β-catenin pathways. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-021-02058-7.
Collapse
Affiliation(s)
- Yakun Luo
- Université Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, 69008, Lyon, France
| | - Virginie Vlaeminck-Guillem
- Université Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, 69008, Lyon, France.,Centre de biologie Sud, Hôpital Lyon Sud, Hospices Civils de Lyon, 69310, Pierre-Bénite, France
| | - Silvère Baron
- Université Clermont Auvergne, GReD, CNRS UMR 6293, INSERM U1103, 28 Place Henri Dunant, BP38, 63001, Clermont-Ferrand, France
| | - Sarah Dallel
- Université Clermont Auvergne, GReD, CNRS UMR 6293, INSERM U1103, 28 Place Henri Dunant, BP38, 63001, Clermont-Ferrand, France
| | - Chang Xian Zhang
- Université Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, 69008, Lyon, France.
| | - Muriel Le Romancer
- Université Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, 69008, Lyon, France
| |
Collapse
|
40
|
Lange M, Begolli R, Giakountis A. Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine. Noncoding RNA 2021; 7:47. [PMID: 34449663 PMCID: PMC8395730 DOI: 10.3390/ncrna7030047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/26/2021] [Accepted: 08/01/2021] [Indexed: 12/11/2022] Open
Abstract
The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.
Collapse
Affiliation(s)
- Marios Lange
- Department of Biochemistry and Biotechnology, University of Thessaly, Biopolis, 41500 Larissa, Greece; (M.L.); (R.B.)
| | - Rodiola Begolli
- Department of Biochemistry and Biotechnology, University of Thessaly, Biopolis, 41500 Larissa, Greece; (M.L.); (R.B.)
| | - Antonis Giakountis
- Department of Biochemistry and Biotechnology, University of Thessaly, Biopolis, 41500 Larissa, Greece; (M.L.); (R.B.)
- Institute for Fundamental Biomedical Research, B.S.R.C “Alexander Fleming”, 34 Fleming Str., 16672 Vari, Greece
| |
Collapse
|
41
|
Bell NAW, Haynes PJ, Brunner K, de Oliveira TM, Flocco MM, Hoogenboom BW, Molloy JE. Single-molecule measurements reveal that PARP1 condenses DNA by loop stabilization. SCIENCE ADVANCES 2021; 7:7/33/eabf3641. [PMID: 34380612 PMCID: PMC8357241 DOI: 10.1126/sciadv.abf3641] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 06/22/2021] [Indexed: 05/11/2023]
Abstract
Poly(ADP-ribose) polymerase 1 (PARP1) is an abundant nuclear enzyme that plays important roles in DNA repair, chromatin organization and transcription regulation. Although binding and activation of PARP1 by DNA damage sites has been extensively studied, little is known about how PARP1 binds to long stretches of undamaged DNA and how it could shape chromatin architecture. Here, using single-molecule techniques, we show that PARP1 binds and condenses undamaged, kilobase-length DNA subject to sub-piconewton mechanical forces. Stepwise decondensation at high force and DNA braiding experiments show that the condensation activity is due to the stabilization of DNA loops by PARP1. PARP inhibitors do not affect the level of condensation of undamaged DNA but act to block condensation reversal for damaged DNA in the presence of NAD+ Our findings suggest a mechanism for PARP1 in the organization of chromatin structure.
Collapse
Affiliation(s)
- Nicholas A W Bell
- The Francis Crick Institute, London NW1 1AT, UK.
- London Centre for Nanotechnology, University College London, London WC1H 0AH, UK
| | - Philip J Haynes
- London Centre for Nanotechnology, University College London, London WC1H 0AH, UK
- Molecular Sciences Research Hub, Department of Chemistry, Imperial College London, London W12 0BZ, UK
- Department of Physics and Astronomy, University College London, London WC1E 6BT, UK
| | - Katharina Brunner
- The Francis Crick Institute, London NW1 1AT, UK
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Taiana Maia de Oliveira
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Maria M Flocco
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Bart W Hoogenboom
- London Centre for Nanotechnology, University College London, London WC1H 0AH, UK
- Department of Physics and Astronomy, University College London, London WC1E 6BT, UK
| | | |
Collapse
|
42
|
Jerkovic I, Cavalli G. Understanding 3D genome organization by multidisciplinary methods. Nat Rev Mol Cell Biol 2021; 22:511-528. [PMID: 33953379 DOI: 10.1038/s41580-021-00362-w] [Citation(s) in RCA: 151] [Impact Index Per Article: 50.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2021] [Indexed: 02/03/2023]
Abstract
Understanding how chromatin is folded in the nucleus is fundamental to understanding its function. Although 3D genome organization has been historically difficult to study owing to a lack of relevant methodologies, major technological breakthroughs in genome-wide mapping of chromatin contacts and advances in imaging technologies in the twenty-first century considerably improved our understanding of chromosome conformation and nuclear architecture. In this Review, we discuss methods of 3D genome organization analysis, including sequencing-based techniques, such as Hi-C and its derivatives, Micro-C, DamID and others; microscopy-based techniques, such as super-resolution imaging coupled with fluorescence in situ hybridization (FISH), multiplex FISH, in situ genome sequencing and live microscopy methods; and computational and modelling approaches. We describe the most commonly used techniques and their contribution to our current knowledge of nuclear architecture and, finally, we provide a perspective on up-and-coming methods that open possibilities for future major discoveries.
Collapse
Affiliation(s)
- Ivana Jerkovic
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France
| | - Giacomo Cavalli
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France.
| |
Collapse
|
43
|
Davidson IF, Peters JM. Genome folding through loop extrusion by SMC complexes. Nat Rev Mol Cell Biol 2021; 22:445-464. [PMID: 33767413 DOI: 10.1038/s41580-021-00349-7] [Citation(s) in RCA: 218] [Impact Index Per Article: 72.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 02/02/2023]
Abstract
Genomic DNA is folded into loops and topologically associating domains (TADs), which serve important structural and regulatory roles. It has been proposed that these genomic structures are formed by a loop extrusion process, which is mediated by structural maintenance of chromosomes (SMC) protein complexes. Recent single-molecule studies have shown that the SMC complexes condensin and cohesin are indeed able to extrude DNA into loops. In this Review, we discuss how the loop extrusion hypothesis can explain key features of genome architecture; cellular functions of loop extrusion, such as separation of replicated DNA molecules, facilitation of enhancer-promoter interactions and immunoglobulin gene recombination; and what is known about the mechanism of loop extrusion and its regulation, for example, by chromatin boundaries that depend on the DNA binding protein CTCF. We also discuss how the loop extrusion hypothesis has led to a paradigm shift in our understanding of both genome architecture and the functions of SMC complexes.
Collapse
Affiliation(s)
- Iain F Davidson
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
| | - Jan-Michael Peters
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria.
| |
Collapse
|
44
|
MacKay K, Kusalik A. Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data. Brief Funct Genomics 2021; 19:292-308. [PMID: 32353112 PMCID: PMC7388788 DOI: 10.1093/bfgp/elaa004] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/30/2020] [Accepted: 02/07/2020] [Indexed: 12/19/2022] Open
Abstract
The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.
Collapse
|
45
|
Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, Shimoyama R, Komatsu M, Hamamoto R. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Front Oncol 2021; 11:666937. [PMID: 34055633 PMCID: PMC8149908 DOI: 10.3389/fonc.2021.666937] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.
Collapse
Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| |
Collapse
|
46
|
Ouyang W, Zhang X, Peng Y, Zhang Q, Cao Z, Li G, Li X. Rapid and Low-Input Profiling of Histone Marks in Plants Using Nucleus CUT&Tag. FRONTIERS IN PLANT SCIENCE 2021; 12:634679. [PMID: 33912205 PMCID: PMC8072009 DOI: 10.3389/fpls.2021.634679] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/19/2021] [Indexed: 05/26/2023]
Abstract
Characterizing genome-wide histone posttranscriptional modifications and transcriptional factor occupancy is crucial for deciphering their biological functions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a powerful method for genome-wide profiling of histone modifications and transcriptional factor-binding sites. However, the current ChIP-seq experimental procedure in plants requires significant material and several days for completion. CUT&Tag is an alternative method of ChIP-seq for low-sample and single-cell epigenomic profiling using protein A-Tn5 transposase fusion proteins (PAT). In this study, we developed a nucleus CUT&Tag (nCUT&Tag) protocol based on the live-cell CUT&Tag technology. Our results indicate that nCUT&Tag could be used for histone modifications profiling in both monocot rice and dicot rapeseed using crosslinked or fresh tissues. In addition, both active and repressive histone marks such as H3K4me3 and H3K9me2 can be identified using our nCUT&Tag. More importantly, all the steps in nCUT&Tag can be finished in only 1 day, and the assay can be performed with as little as 0.01 g of plant tissue as starting materials. Therefore, our results demonstrate that nCUT&Tag is an efficient alternative strategy for plant epigenomic studies.
Collapse
Affiliation(s)
- Weizhi Ouyang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiwen Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Qing Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhilin Cao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Department of Resources and Environment, Henan University of Engineering, Zhengzhou, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan, China
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| |
Collapse
|
47
|
Jung YL, Kirli K, Alver BH, Park PJ. Resources and challenges for integrative analysis of nuclear architecture data. Curr Opin Genet Dev 2021; 67:103-110. [PMID: 33450522 PMCID: PMC8084903 DOI: 10.1016/j.gde.2020.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/09/2020] [Accepted: 12/13/2020] [Indexed: 11/22/2022]
Abstract
A large amount of genomic data for profiling three-dimensional genome architecture have accumulated from large-scale consortium projects as well as from individual laboratories. In this review, we summarize recent landmark datasets and collections in the field. We describe the challenges in collection, annotation, and analysis of these data, particularly for integration of sequencing and microscopy data. We introduce efforts from consortia and independent groups to harmonize diverse datasets. As the resolution and throughput of sequencing and imaging technologies continue to increase, more efficient utilization and integration of collected data will be critical for a better understanding of nuclear architecture.
Collapse
Affiliation(s)
- Youngsook L Jung
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Koray Kirli
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Burak H Alver
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
48
|
Pan Q, Liu YJ, Bai XF, Han XL, Jiang Y, Ai B, Shi SS, Wang F, Xu MC, Wang YZ, Zhao J, Chen JX, Zhang J, Li XC, Zhu J, Zhang GR, Wang QY, Li CQ. VARAdb: a comprehensive variation annotation database for human. Nucleic Acids Res 2021; 49:D1431-D1444. [PMID: 33095866 PMCID: PMC7779011 DOI: 10.1093/nar/gkaa922] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 09/28/2020] [Accepted: 10/22/2020] [Indexed: 01/08/2023] Open
Abstract
With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include ‘Variation information’, ‘Regulatory information’, ‘Related genes’, ‘Chromatin accessibility’ and ‘Chromatin interaction’. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.
Collapse
Affiliation(s)
- Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yue-Juan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xue-Feng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xiao-Le Han
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yong Jiang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Shan-Shan Shi
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Fan Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Ming-Cong Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yue-Zhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jun Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jia-Xin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xue-Cang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Guo-Rui Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Qiu-Yu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Chun-Quan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| |
Collapse
|
49
|
Mills C, Muruganujan A, Ebert D, Marconett CN, Lewinger JP, Thomas PD, Mi H. PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence. PLoS One 2020; 15:e0243791. [PMID: 33320871 PMCID: PMC7737992 DOI: 10.1371/journal.pone.0243791] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/25/2020] [Indexed: 12/28/2022] Open
Abstract
Enhancers are powerful and versatile agents of cell-type specific gene regulation, which are thought to play key roles in human disease. Enhancers are short DNA elements that function primarily as clusters of transcription factor binding sites that are spatially coordinated to regulate expression of one or more specific target genes. These regulatory connections between enhancers and target genes can therefore be characterized as enhancer-gene links that can affect development, disease, and homeostatic cellular processes. Despite their implication in disease and the establishment of cell identity during development, most enhancer-gene links remain unknown. Here we introduce a new, publicly accessible database of predicted enhancer-gene links, PEREGRINE. The PEREGRINE human enhancer-gene links interactive web interface incorporates publicly available experimental data from ChIA-PET, eQTL, and Hi-C assays across 78 cell and tissue types to link 449,627 enhancers to 17,643 protein-coding genes. These enhancer-gene links are made available through the new Enhancer module of the PANTHER database and website where the user may easily access the evidence for each enhancer-gene link, as well as query by target gene and enhancer location.
Collapse
Affiliation(s)
- Caitlin Mills
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Anushya Muruganujan
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Dustin Ebert
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Crystal N. Marconett
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine USC, Los Angeles, CA, United States of America
- Norris Cancer Center, Keck School of Medicine USC, Los Angeles, CA, United States of America
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Paul D. Thomas
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
- * E-mail:
| |
Collapse
|
50
|
Ouyang W, Xiong D, Li G, Li X. Unraveling the 3D Genome Architecture in Plants: Present and Future. MOLECULAR PLANT 2020; 13:1676-1693. [PMID: 33065269 DOI: 10.1016/j.molp.2020.10.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 08/09/2020] [Accepted: 10/08/2020] [Indexed: 05/02/2023]
Abstract
The eukaryotic genome has a hierarchical three-dimensional (3D) organization with functional implications for DNA replication, DNA repair, and transcriptional regulation. Over the past decade, scientists have endeavored to elucidate the spatial characteristics and functions of plant genome architecture using high-throughput chromatin conformation capturing technologies such as Hi-C, ChIA-PET, and HiChIP. Here, we systematically review current understanding of chromatin organization in plants at multiple scales. We also discuss the emerging opinions and concepts in 3D genome research, focusing on state-of-the-art 3D genome techniques, RNA-chromatin interactions, liquid-liquid phase separation, and dynamic chromatin alterations. We propose the application of single-cell/single-molecule multi-omics, multiway (DNA-DNA, DNA-RNA, and RNA-RNA interactions) chromatin conformation capturing methods, and proximity ligation-independent 3D genome-mapping technologies to explore chromatin organization structure and function in plants. Such methods could reveal the spatial interactions between trait-related SNPs and their target genes at various spatiotemporal resolutions, and elucidate the molecular mechanisms of the interactions among DNA elements, RNA molecules, and protein factors during the formation of key traits in plants.
Collapse
Affiliation(s)
- Weizhi Ouyang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Dan Xiong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
| |
Collapse
|