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Li Z, Schlick T. Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics. Nucleic Acids Res 2024; 52:583-599. [PMID: 38015443 PMCID: PMC10810283 DOI: 10.1093/nar/gkad1121] [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: 07/06/2023] [Revised: 10/12/2023] [Accepted: 11/22/2023] [Indexed: 11/29/2023] Open
Abstract
The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXA and Fbn2 mouse genes ranging in size from 50 to 100 kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.
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Affiliation(s)
- Zilong Li
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003, USA
- Simons Center for Computational Physical Chemistry, 24 Waverly Place, Silver Building, New York University, New York, NY 10003, USA
| | - Tamar Schlick
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003, USA
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
- Simons Center for Computational Physical Chemistry, 24 Waverly Place, Silver Building, New York University, New York, NY 10003, USA
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Liu T, Qiu QT, Hua KJ, Ma BG. Chromosome structure modeling tools and their evaluation in bacteria. Brief Bioinform 2024; 25:bbae044. [PMID: 38385874 PMCID: PMC10883143 DOI: 10.1093/bib/bbae044] [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: 10/30/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.
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Affiliation(s)
- Tong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qin-Tian Qiu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Kang-Jian Hua
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Wang X, Gu WC, Li J, Ma BG. EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient. BIOINFORMATICS (OXFORD, ENGLAND) 2023; 39:btad638. [PMID: 37847746 DOI: 10.1093/bioinformatics/btad638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/28/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023]
Abstract
MOTIVATION Reconstruction of 3D structure models is of great importance for the study of chromosome function. Software tools for this task are highly needed. RESULTS We present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. As an update of our previous EVR algorithm, EVRC now can deal with both single and multiple chromosomes in structure modeling. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions. AVAILABILITY AND IMPLEMENTATION The software is available at https://github.com/mbglab/EVRC.
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Affiliation(s)
- Xiao Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei-Cheng Gu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jie Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Li Z, Portillo-Ledesma S, Schlick T. Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data. Curr Opin Cell Biol 2023; 83:102209. [PMID: 37506571 PMCID: PMC10529954 DOI: 10.1016/j.ceb.2023.102209] [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: 05/15/2023] [Revised: 06/07/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Chromosome conformation capture technologies that provide frequency information for contacts between genomic regions have been crucial for increasing our understanding of genome folding and regulation. However, such data do not provide direct evidence of the spatial 3D organization of chromatin. In this opinion article, we discuss the development and application of computational methods to reconstruct chromatin 3D structures from experimental 2D contact data, highlighting how such modeling provides biological insights and can suggest mechanisms anchored to experimental data. By applying different reconstruction methods to the same contact data, we illustrate some state-of-the-art of these techniques and discuss our gene resolution approach based on Brownian dynamics and Monte Carlo sampling.
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Affiliation(s)
- Zilong Li
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Stephanie Portillo-Ledesma
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, 10012, NY, USA; New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Room 340, Geography Building, 3663 North Zhongshan Road, Shanghai, 200122, China; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA.
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The Spatial Organization of Bacterial Transcriptional Regulatory Networks. Microorganisms 2022; 10:microorganisms10122366. [PMID: 36557619 PMCID: PMC9787925 DOI: 10.3390/microorganisms10122366] [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: 10/27/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
The transcriptional regulatory network (TRN) is the central pivot of a prokaryotic organism to receive, process and respond to internal and external environmental information. However, little is known about its spatial organization so far. In recent years, chromatin interaction data of bacteria such as Escherichia coli and Bacillus subtilis have been published, making it possible to study the spatial organization of bacterial transcriptional regulatory networks. By combining TRNs and chromatin interaction data of E. coli and B. subtilis, we explored the spatial organization characteristics of bacterial TRNs in many aspects such as regulation directions (positive and negative), central nodes (hubs, bottlenecks), hierarchical levels (top, middle, bottom) and network motifs (feed-forward loops and single input modules) of the TRNs and found that the bacterial TRNs have a variety of stable spatial organization features under different physiological conditions that may be closely related with biological functions. Our findings provided new insights into the connection between transcriptional regulation and the spatial organization of chromosome in bacteria and might serve as a factual foundation for trying spatial-distance-based gene circuit design in synthetic biology.
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Intelligent Three-Dimensional Reconstruction Algorithm-Based Ultrasound-Guided Nerve Block in Intraoperative Anesthesia and Postoperative Analgesia of Orthopedic Surgery. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9447649. [PMID: 35912159 PMCID: PMC9337952 DOI: 10.1155/2022/9447649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/24/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed at analyzing the role of ultrasound-guided nerve block based on intelligent three-dimensional (3D) reconstruction algorithm in intraoperative anesthesia and postoperative analgesia of orthopedic surgery. 68 elderly patients were undergoing orthopedic surgery on the lower extremities, and they were randomly rolled into two groups with 34 patients in each group. The patients in control group received sciatic nerve block anesthesia (SNBA), and the patients in the experimental group received ultrasound-guided SNBA (UG-SNBA) under 3D reconstruction algorithm to analyze and compare the anesthesia effect and the postoperative analgesia effect. The results showed that compared with other algorithms, the evaluation index of ultrasound images processed by the 3D reconstruction algorithm was better. In terms of anesthesia effect, there was no significant difference in systolic blood pressure, diastolic blood pressure, and heart rate between the two groups before surgery (
). Intraoperative and postoperative indicators of the experimental group were significantly better than those of the control group; the drug dosage (61 mg) was less than that of the control group (78 mg). In addition, the onset time of anesthesia, the time of pain blockade, and the postoperative awake time (5 minutes, 8 minutes, and 8 minutes, respectively) were shorter than those in the control group (13 minutes, 15 minutes, and 15 minutes, respectively). The visual analogue scale (VAS) scores of the experimental group were better than those of the control group on the day after surgery, one day after surgery, two days after surgery, and three days after surgery, with significant differences (
). In summary, 3D reconstruction algorithm-based ultrasound image effect was clearer, the effect of UG-SNBA was more stable, and the postoperative analgesic effect was better. This work provided a higher reference for the selection of safe and effective anesthesia options in orthopedic surgery.
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Gilbert BR, Thornburg ZR, Lam V, Rashid FZM, Glass JI, Villa E, Dame RT, Luthey-Schulten Z. Generating Chromosome Geometries in a Minimal Cell From Cryo-Electron Tomograms and Chromosome Conformation Capture Maps. Front Mol Biosci 2021; 8:644133. [PMID: 34368224 PMCID: PMC8339304 DOI: 10.3389/fmolb.2021.644133] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 05/14/2021] [Indexed: 12/31/2022] Open
Abstract
JCVI-syn3A is a genetically minimal bacterial cell, consisting of 493 genes and only a single 543 kbp circular chromosome. Syn3A’s genome and physical size are approximately one-tenth those of the model bacterial organism Escherichia coli’s, and the corresponding reduction in complexity and scale provides a unique opportunity for whole-cell modeling. Previous work established genome-scale gene essentiality and proteomics data along with its essential metabolic network and a kinetic model of genetic information processing. In addition to that information, whole-cell, spatially-resolved kinetic models require cellular architecture, including spatial distributions of ribosomes and the circular chromosome’s configuration. We reconstruct cellular architectures of Syn3A cells at the single-cell level directly from cryo-electron tomograms, including the ribosome distributions. We present a method of generating self-avoiding circular chromosome configurations in a lattice model with a resolution of 11.8 bp per monomer on a 4 nm cubic lattice. Realizations of the chromosome configurations are constrained by the ribosomes and geometry reconstructed from the tomograms and include DNA loops suggested by experimental chromosome conformation capture (3C) maps. Using ensembles of simulated chromosome configurations we predict chromosome contact maps for Syn3A cells at resolutions of 250 bp and greater and compare them to the experimental maps. Additionally, the spatial distributions of ribosomes and the DNA-crowding resulting from the individual chromosome configurations can be used to identify macromolecular structures formed from ribosomes and DNA, such as polysomes and expressomes.
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Affiliation(s)
- Benjamin R Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zane R Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Vinson Lam
- Division of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Fatema-Zahra M Rashid
- Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands.,Center for Microbial Cell Biology, Leiden University, Leiden, Netherlands
| | - John I Glass
- Synthetic Biology Group, J. Craig Venter Institute, La Jolla, CA, United States
| | - Elizabeth Villa
- Division of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Remus T Dame
- Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands.,Center for Microbial Cell Biology, Leiden University, Leiden, Netherlands
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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Gong H, Yang Y, Zhang S, Li M, Zhang X. Application of Hi-C and other omics data analysis in human cancer and cell differentiation research. Comput Struct Biotechnol J 2021; 19:2070-2083. [PMID: 33995903 PMCID: PMC8086027 DOI: 10.1016/j.csbj.2021.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.
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Affiliation(s)
- Haiyan Gong
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
| | - Yi Yang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
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Oluwadare O, Highsmith M, Turner D, Lieberman Aiden E, Cheng J. GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data. BMC Mol Cell Biol 2020; 21:60. [PMID: 32758136 PMCID: PMC7405446 DOI: 10.1186/s12860-020-00304-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 07/29/2020] [Indexed: 11/10/2022] Open
Abstract
Advances in the study of chromosome conformation capture technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional chromosome and genome structure reconstruction methods from Hi-C data. The three dimensional genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed. In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. The GSDB contains over 50,000 structures from 12 state-of-the-art Hi-C data structure prediction algorithms for 32 Hi-C datasets. GSDB functions as a centralized collection of genome structures which will enable the exploration of the dynamic architectures of chromosomes and genomes for biomedical research. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB
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Affiliation(s)
- Oluwatosin Oluwadare
- Department of Computer Science, University of Colorado, Colorado Springs, CO, 80918, USA
| | - Max Highsmith
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Douglass Turner
- Elastic Image Software LLC, 21 Walnut Street, Lexington, MA, 02421, USA
| | | | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
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