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Journal articles on the topic 'Chia-PET'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Li, Sun, Chang, Cai, Hong, and Zhou. "Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)." Genes 10, no. 7 (July 22, 2019): 554. http://dx.doi.org/10.3390/genes10070554.

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Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes short-read and long-read ChIA-PET data with multithreading and generates statistics of results in an HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze RNA polymerase II (RNAPII) ChIA-PET data from human K562 cells with it. We compared our tool with existing tools, including ChiaSig, MICC, Mango and ChIA-PET2, by using the same public data set in the same computer. Most peaks detected by the ChIA-PET Tool V3 overlap with those of other tools. There is higher enrichment for significant chromatin interactions from ChIA-PET Tool V3 in aggregate peak analysis (APA) plots. The ChIA-PET Tool V3 is publicly available at GitHub.
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2

Lee, Byoungkoo, Jiahui Wang, Liuyang Cai, Minji Kim, Sandeep Namburi, Harianto Tjong, Yuliang Feng, et al. "ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization." Science Advances 6, no. 28 (July 2020): eaay2078. http://dx.doi.org/10.1126/sciadv.aay2078.

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ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensional contact map viewing with Juicebox and HiGlass and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. To enable structural interpretation, ChIA-PIPE calls chromatin contact domains, resolves allele-specific peaks and loops, and annotates enhancer-promoter loops. ChIA-PIPE also supports the analysis of other related chromatin-mapping data types.
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3

Hershey, David. "Don't Just Pet Your Chia." Science Activities: Classroom Projects and Curriculum Ideas 32, no. 2 (June 1, 1995): 8–12. http://dx.doi.org/10.1080/00368121.1995.10113179.

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4

Vardaxis, Ioannis, Finn Drabløs, Morten B. Rye, and Bo Henry Lindqvist. "MACPET: model-based analysis for ChIA-PET." Biostatistics 21, no. 3 (January 30, 2019): 625–39. http://dx.doi.org/10.1093/biostatistics/kxy084.

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Summary We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS. MACPET is freely available on Bioconductor. ChIA-PET; MACPET; Model-based clustering; Paired-end tags; Peak-calling algorithm.
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5

Small, Ernest. "34. Chia – not just a pet." Biodiversity 12, no. 1 (March 2011): 49–56. http://dx.doi.org/10.1080/14888386.2011.575104.

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6

Li, Guipeng, Yang Chen, Michael P. Snyder, and Michael Q. Zhang. "ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis." Nucleic Acids Research 45, no. 1 (September 12, 2016): e4-e4. http://dx.doi.org/10.1093/nar/gkw809.

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7

Zhang, Jingyao, Huay Mei Poh, Su Qin Peh, Yee Yen Sia, Guoliang Li, Fabianus Hendriyan Mulawadi, Yufen Goh, et al. "ChIA-PET analysis of transcriptional chromatin interactions." Methods 58, no. 3 (November 2012): 289–99. http://dx.doi.org/10.1016/j.ymeth.2012.08.009.

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8

He, Chao, Guipeng Li, Diekidel M. Nadhir, Yang Chen, Xiaowo Wang, and Michael Q. Zhang. "Advances in computational ChIA-PET data analysis." Quantitative Biology 4, no. 3 (September 2016): 217–25. http://dx.doi.org/10.1007/s40484-016-0080-3.

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9

Phanstiel, Douglas H., Alan P. Boyle, Nastaran Heidari, and Michael P. Snyder. "Mango: a bias-correcting ChIA-PET analysis pipeline." Bioinformatics 31, no. 19 (June 1, 2015): 3092–98. http://dx.doi.org/10.1093/bioinformatics/btv336.

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10

Huang, Weichun, Mario Medvedovic, Jingwen Zhang, and Liang Niu. "ChIAPoP: a new tool for ChIA-PET data analysis." Nucleic Acids Research 47, no. 7 (February 8, 2019): e37-e37. http://dx.doi.org/10.1093/nar/gkz062.

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11

Capurso, Dan, Zhonghui Tang, and Yijun Ruan. "Methods for comparative ChIA-PET and Hi-C data analysis." Methods 170 (January 2020): 69–74. http://dx.doi.org/10.1016/j.ymeth.2019.09.019.

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12

Zhang, Henry B., Minji Kim, Jeffrey H. Chuang, and Yijun Ruan. "pyBedGraph: a python package for fast operations on 1D genomic signal tracks." Bioinformatics 36, no. 10 (February 11, 2020): 3234–35. http://dx.doi.org/10.1093/bioinformatics/btaa061.

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Abstract Motivation Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. Results We developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or a bigWig file. When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average 260 times faster than the existing program pyBigWig. On average, pyBedGraph can look up the exact mean signal of 1 million regions in ∼0.26 s and can compute their approximate means in <0.12 s on a conventional laptop. Availability and implementation pyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.
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13

Orlov, Y. L., O. Thierry, A. G. Bogomolov, A. V. Tsukanov, E. V. Kulakova, E. R. Galieva, A. O. Bragin, and G. Li. "Computer methods of analysis of chromosome contacts in the cell nucleus based on sequencing technology data." Biomeditsinskaya Khimiya 63, no. 5 (2017): 418–22. http://dx.doi.org/10.18097/pbmc20176305418.

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The study spatial chromosome structure and chromosome folding in the interphase cell nucleus is an important challenge of world science. Detection of eukaryotic genome regions that physically interact with each other could be done by modern sequencing technologies. A basic method of chromosome folding by total sequencing of contacting DNA fragments is HI-C. Long-range chromosomal interactions play an important role in gene transcription and regulation. The study of chromosome interactions, 3D (three-dimensional) genome structure and its effect on gene transcription allows revealing fundamental biological processes from a viewpoint of structural regulation and are important for cancer research. The technique of chromatin immunoprecipitation and subsequent sequencing (ChIP-seq) make possible to determine binding sites of transcription factors that regulate expression of eukaryotic genes; genome transcription factors binding maps have been. The ChIA-PET technology allows exploring not only target protein binding sites, but also pairs of such sites on proximally located and interacting with each other chromosomes co-located in three-dimensional space of the cell nucleus. Here we discuss the principles of the construction of genomic maps and matrices of chromosome contacts according to ChIA-PET and Hi-C data that capture the chromosome conformation and overview existing software for 3D genome analysis including in house programs of gene location analysis in topological domains.
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14

Li, Guoliang, Melissa J. Fullwood, Han Xu, Fabianus Hendriyan Mulawadi, Stoyan Velkov, Vinsensius Vega, Pramila Nuwantha Ariyaratne, et al. "ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing." Genome Biology 11, no. 2 (2010): R22. http://dx.doi.org/10.1186/gb-2010-11-2-r22.

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15

Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2018, no. 8 (June 12, 2018): pdb.prot104620. http://dx.doi.org/10.1101/pdb.prot104620.

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16

Fan, Xiucheng. "The Role of Transcription Factor Tfii-I/GTF2I in the Response to Cellular Stress in Hematopoietic Cells." Blood 124, no. 21 (December 6, 2014): 2915. http://dx.doi.org/10.1182/blood.v124.21.2915.2915.

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The transcription factor ATF3 and the heme regulated eIF2α kinase (HRI) have previously been shown to be involved in the stress response in hematopoietic cells. Many stress-responsive genes are regulated at the level of transcription elongation and are characterized by paused RNA polymerase II (Pol II) at the promoter region. Indeed, analyzing data from the public ENCODE project revealed that the HRI and the ATF3 genes contained paused Pol II at the promoter and at upstream intergenic regulatory elements. In this study we performed a genomic and proteomic analysis of transcription factor TFII-I (also known as GTF2I) in human erythroleukemia K562 cells. TFII-I is a ubiquitously expressed transcription factor previously implicated in regulating genes involved in stress response. TFII-I exhibits both positive and negative effects on transcription. TFII-I is a relatively large protein (about 120 Kda) and contains DNA binding, nuclear localization, and multiple protein/protein interaction domains. Using biotinylation tagging technology and high-throughput sequencing, we determined sites of chromatin interactions for TFII-I. This study revealed that TFII-I binds downstream of Pol II peaks at the ATF3 and HRI gene loci (Fig 1). A proteomic analysis revealed that TFII-I interacts with the elongation factor elongin A, which has been shown to help Pol II overcome the paused state. We demonstrate that Elongin A only associates with the ATF3 gene in response to cellular stress. The stress induction of ATF3 gene transcription requires the function of TFII-I. Furthermore, in the absence of induction, transcription factors E2F6, and E2F4 as well as the histone deacetylase I (HDAC1) bind to the HRI and ATF3 genes in close proximity to the Pol II and TFII-I peaks (Fig. 1). E2F4/6 and HDAC1 have been implicated in the repression of gene expression. The DNA elements that recruit TFII-I, Pol II and the repressor proteins interact over long distances in K562 cells as shown by ChIA-Pet data available from the ENCODE project (Fig.1). Moreover, we demonstrate that TFII-I directly interacts with E2F6 using co-immunoprecipitation assays (Fig. 2). Partial depletion of TFII-I by shRNA led to a decrease in stress mediated transcription without impairing basal transcription levels. The data demonstrate that TFII-I regulates stress-responsive transcription of the HRI and ATF3 genes in hematopoietic cells. In the absence of stress, TFII-I occupies promoter and long distance regulatory elements together with Pol II, E2F6/E2F4, and HDAC1. These proteins may be part of a chromatin loop that keeps Pol II in a repressed configuration. We suggest that upon stress induction, TFII-I recruits Elongin A, which converts Pol II into a transcriptionally active configuration. Transcription elongation disrupts formation of a repressive chromatin loop and leads to transcription activation of the HRI and ATF3 genes. Figure 1 Diagram of the HRI and ATF3 gene loci showing interactions of TFII-I, Pol II, E2F4, E2F6, and HDAC1 as well as RNA seq, and Pol II ChIA-PET data. The ChIA-PET data show interactions of chromatin fragments that have Pol II bound. The arrows indicate the direction of transcription of the HRI gene and from the two promoters of the ATF3 gene (P1 and P2). Figure 1. Diagram of the HRI and ATF3 gene loci showing interactions of TFII-I, Pol II, E2F4, E2F6, and HDAC1 as well as RNA seq, and Pol II ChIA-PET data. The ChIA-PET data show interactions of chromatin fragments that have Pol II bound. The arrows indicate the direction of transcription of the HRI gene and from the two promoters of the ATF3 gene (P1 and P2). Figure 2 Input; TFII-I Figure 2. Input; TFII-I Figure 3 Co-Immunoprecipitation experiment showing the interaction between TFII-I and E2F6. K562 cell nuclear extracts were subjected to precipitation with IgG control or TFII-I specific antibodies. The material was fractionated by SDS-PAGE, transferred to a nylon membrane and probed with an antibody specific for E2F6. Figure 3. Co-Immunoprecipitation experiment showing the interaction between TFII-I and E2F6. K562 cell nuclear extracts were subjected to precipitation with IgG control or TFII-I specific antibodies. The material was fractionated by SDS-PAGE, transferred to a nylon membrane and probed with an antibody specific for E2F6. Disclosures No relevant conflicts of interest to declare.
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17

Krismer, Konstantin, Yuchun Guo, and David K. Gifford. "IDR2D identifies reproducible genomic interactions." Nucleic Acids Research 48, no. 6 (February 3, 2020): e31-e31. http://dx.doi.org/10.1093/nar/gkaa030.

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Abstract Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.
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18

Li, Xingwang, Oscar Junhong Luo, Ping Wang, Meizhen Zheng, Danjuan Wang, Emaly Piecuch, Jacqueline Jufen Zhu, et al. "Long-read ChIA-PET for base-pair-resolution mapping of haplotype-specific chromatin interactions." Nature Protocols 12, no. 5 (March 30, 2017): 899–915. http://dx.doi.org/10.1038/nprot.2017.012.

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19

Paulsen, Jonas, Einar A. Rødland, Lars Holden, Marit Holden, and Eivind Hovig. "A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions." Nucleic Acids Research 42, no. 18 (August 11, 2014): e143-e143. http://dx.doi.org/10.1093/nar/gku738.

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20

Lun, Aaron T. L., Malcolm Perry, and Elizabeth Ing-Simmons. "Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments." F1000Research 5 (May 20, 2016): 950. http://dx.doi.org/10.12688/f1000research.8759.1.

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The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.
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21

Lun, Aaron T. L., Malcolm Perry, and Elizabeth Ing-Simmons. "Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments." F1000Research 5 (June 28, 2016): 950. http://dx.doi.org/10.12688/f1000research.8759.2.

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The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.
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22

Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Chromatin Immunoprecipitation for Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2018, no. 8 (June 12, 2018): pdb.prot097725. http://dx.doi.org/10.1101/pdb.prot097725.

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23

Kulakova, Ekaterina, Anastasia Spitsina, Anton Bogomolov, Nina Orlova, Artur Dergilev, Irina Chadaeva, Vladimir Babenko, and Yuriy Orlov. "Program for analysis of genome distribution of chromosome contacts in cell nucleus by the data obtained using ChIA-PET and Hi-C technologies." Program Systems: Theory and Applications 8, no. 1 (2017): 219–42. http://dx.doi.org/10.25209/2079-3316-2017-8-1-219-242.

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24

Kulakova, Ye, A. Spitsina, N. Orlova, A. Dergilev, A. Svichkarev, N. Safronova, I. Chernykh, and Yu Orlov. "Supercomputer analysis of genomics and transcriptomics data revealed by high-throughput DNA sequencing." Program Systems: Theory and Applications 6, no. 2 (2015): 129–48. http://dx.doi.org/10.25209/2079-3316-2015-6-2-129-148.

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Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Corrigendum: Chromatin Immunoprecipitation for Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2020, no. 1 (January 2020): pdb.corr106765. http://dx.doi.org/10.1101/pdb.corr106765.

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Buisine, Nicolas, Xiaoan Ruan, Patrice Bilesimo, Alexis Grimaldi, Gladys Alfama, Pramila Ariyaratne, Fabianus Mulawadi, et al. "Xenopus tropicalis Genome Re-Scaffolding and Re-Annotation Reach the Resolution Required for In Vivo ChIA-PET Analysis." PLOS ONE 10, no. 9 (September 8, 2015): e0137526. http://dx.doi.org/10.1371/journal.pone.0137526.

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27

Wang, Siguo, Qinhu Zhang, Ying He, Zhen Cui, Zhenghao Guo, Kyungsook Han, and De-Shuang Huang. "DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes." PLOS Computational Biology 18, no. 10 (October 7, 2022): e1010572. http://dx.doi.org/10.1371/journal.pcbi.1010572.

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In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.
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Mills, Caitlin, Anushya Muruganujan, Dustin Ebert, Crystal N. Marconett, Juan Pablo Lewinger, Paul D. Thomas, and Huaiyu Mi. "PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence." PLOS ONE 15, no. 12 (December 15, 2020): e0243791. http://dx.doi.org/10.1371/journal.pone.0243791.

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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.
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Yousif, Faris H., Bakhtiar Q. Aziz, and Ezaddin N. Baban. "Subsurface Imaging of the Fatha Formation Utilizing 3D Seismic Data in Chia Surkh Area, Kurdistan Region, Iraq." Iraqi Geological Journal 55, no. 2B (August 31, 2022): 35–46. http://dx.doi.org/10.46717/igj.55.2b.4ms-2022-08-20.

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The current study aims to detect subsurface geologic features using 3D dense sampling seismic data in the Fatha Formation, in the Chia Surkh area, Kurdistan Region,Iraq. A3D cube seismic data covering 75 Km2 were used to image the Fatha Formation subsurface geologic structures. The seismic data and appropriate information were gathered from Pet Oil Company with the permission of the Ministry of Natural Resources of the Kurdistan Region, Iraq. A cube of seismic data was used to image the three units of the Fatha Formation. In this study, forty seismic sections with the direction of NE-SW and 30 crossline sections of NW-SE direction were used. The map shows the existence of several features such as a three-way dip closure elongated NW-SE and extended through the whole study area. The reflector three of the Fatha isochron map shows TWT ranging from 890 ms to 1720 ms. The depth map of Fatha unit 1 Formation, shows depth with seismic reference datum ranging from 838 m to 2334 m. The study concluded the existence of several structural features; a major anticline trending with Zagros belt trend, and three longitudinal systematic reverse faults on the southwest side of the anticline, leading to the possibility for which the area is considered as a prospective oil reservoir, might work as a good trap, an anticline, evaporites work as a seal and limestone rocks as a reservoir.
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Scala, Giovanni, Francesca Gorini, Susanna Ambrosio, Andrea M. Chiariello, Mario Nicodemi, Luigi Lania, Barbara Majello, and Stefano Amente. "8-oxodG accumulation within super-enhancers marks fragile CTCF-mediated chromatin loops." Nucleic Acids Research 50, no. 6 (March 2, 2022): 3292–306. http://dx.doi.org/10.1093/nar/gkac143.

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Abstract 8-Oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG), a major product of the DNA oxidization process, has been proposed to have an epigenetic function in gene regulation and has been associated with genome instability. NGS-based methodologies are contributing to the characterization of the 8-oxodG function in the genome. However, the 8-oxodG epigenetic role at a genomic level and the mechanisms controlling the genomic 8-oxodG accumulation/maintenance have not yet been fully characterized. In this study, we report the identification and characterization of a set of enhancer regions accumulating 8-oxodG in human epithelial cells. We found that these oxidized enhancers are mainly super-enhancers and are associated with bidirectional-transcribed enhancer RNAs and DNA Damage Response activation. Moreover, using ChIA-PET and HiC data, we identified specific CTCF-mediated chromatin loops in which the oxidized enhancer and promoter regions physically associate. Oxidized enhancers and their associated chromatin loops accumulate endogenous double-strand breaks which are in turn repaired by NHEJ pathway through a transcription-dependent mechanism. Our work suggests that 8-oxodG accumulation in enhancers–promoters pairs occurs in a transcription-dependent manner and provides novel mechanistic insights on the intrinsic fragility of chromatin loops containing oxidized enhancers-promoters interactions.
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Sati, Satish, Parker Jones, Hali S. Kim, Linda A. Zhou, Emmanuel Rapp-Reyes, and Thomas H. Leung. "HiCuT: An efficient and low input method to identify protein-directed chromatin interactions." PLOS Genetics 18, no. 3 (March 23, 2022): e1010121. http://dx.doi.org/10.1371/journal.pgen.1010121.

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3D genome organization regulates gene expression, and disruption of these long-range (>20kB) DNA-protein interactions results in pathogenic phenotypes. Chromosome conformation methods in conjunction with chromatin immunoprecipitation were used to decipher protein-directed chromatin interactions. However, these methods required abundant starting material (>500,000 cells), sizable number of sequencing reads (>100 million reads), and elaborate data processing methods to reduce background noise, which limited their use in primary cells. Hi-C Coupled chromatin cleavage and Tagmentation (HiCuT) is a new transposase-assisted tagmentation method that generates high-resolution protein directed long-range chromatin interactions as efficiently as existing methods, HiChIP and ChIA-PET, despite using 100,000 cells (5-fold less) and 12 million sequencing reads (8-fold fewer). Moreover, HiCuT generates high resolution fragment libraries with low background signal that are easily interpreted with minimal computational processing. We used HiCuT in human primary skin cells to link previously identified single nucleotide polymorphisms (SNPs) in skin disease to candidate genes and to identify functionally relevant transcription factors in an unbiased manner. HiCuT broadens the capacity for genomic profiling in systems previously unmeasurable, including primary cells, human tissue samples, and rare cell populations, and may be a useful tool for all investigators studying human genetics and personalized epigenomics.
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White, Shannon M., Michael P. Snyder, and Chunling Yi. "Master lineage transcription factors anchor trans mega transcriptional complexes at highly accessible enhancer sites to promote long-range chromatin clustering and transcription of distal target genes." Nucleic Acids Research 49, no. 21 (November 24, 2021): 12196–210. http://dx.doi.org/10.1093/nar/gkab1105.

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Abstract The term ‘super enhancers’ (SE) has been widely used to describe stretches of closely localized enhancers that are occupied collectively by large numbers of transcription factors (TFs) and co-factors, and control the transcription of highly-expressed genes. Through integrated analysis of >600 DNase-seq, ChIP-seq, GRO-seq, STARR-seq, RNA-seq, Hi-C and ChIA-PET data in five human cancer cell lines, we identified a new class of autonomous SEs (aSEs) that are excluded from classic SE calls by the widely used Rank Ordering of Super-Enhancers (ROSE) method. TF footprint analysis revealed that compared to classic SEs and regular enhancers, aSEs are tightly bound by a dense array of master lineage TFs, which serve as anchors to recruit additional TFs and co-factors in trans. In addition, aSEs are preferentially enriched for Cohesins, which likely involve in stabilizing long-distance interactions between aSEs and their distal target genes. Finally, we showed that aSEs can be reliably predicted using a single DNase-seq data or combined with Mediator and/or P300 ChIP-seq. Overall, our study demonstrates that aSEs represent a unique class of functionally important enhancer elements that distally regulate the transcription of highly expressed genes.
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Wlasnowolski, Michal, Michal Sadowski, Tymon Czarnota, Karolina Jodkowska, Przemyslaw Szalaj, Zhonghui Tang, Yijun Ruan, and Dariusz Plewczynski. "3D-GNOME 2.0: a three-dimensional genome modeling engine for predicting structural variation-driven alterations of chromatin spatial structure in the human genome." Nucleic Acids Research 48, W1 (May 22, 2020): W170—W176. http://dx.doi.org/10.1093/nar/gkaa388.

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Abstract Structural variants (SVs) that alter DNA sequence emerge as a driving force involved in the reorganisation of DNA spatial folding, thus affecting gene transcription. In this work, we describe an improved version of our integrated web service for structural modeling of three-dimensional genome (3D-GNOME), which now incorporates all types of SVs to model changes to the reference 3D conformation of chromatin. In 3D-GNOME 2.0, the default reference 3D genome structure is generated using ChIA-PET data from the GM12878 cell line and SVs data are sourced from the population-scale catalogue of SVs identified by the 1000 Genomes Consortium. However, users may also submit their own structural data to set a customized reference genome structure, and/or a custom input list of SVs. 3D-GNOME 2.0 provides novel tools to inspect, visualize and compare 3D models for regions that differ in terms of their linear genomic sequence. Contact diagrams are displayed to compare the reference 3D structure with the one altered by SVs. In our opinion, 3D-GNOME 2.0 is a unique online tool for modeling and analyzing conformational changes to the human genome induced by SVs across populations. It can be freely accessed at https://3dgnome.cent.uw.edu.pl/.
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Chen, Dijun, Liang-Yu Fu, Zhao Zhang, Guoliang Li, Hang Zhang, Li Jiang, Andrew P. Harrison, et al. "Dissecting the chromatin interactome of microRNA genes." Nucleic Acids Research 42, no. 5 (December 18, 2013): 3028–43. http://dx.doi.org/10.1093/nar/gkt1294.

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Abstract Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA–target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR–MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.
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Pande, Amit, Wojciech Makalowski, Jürgen Brosius, and Carsten A. Raabe. "Enhancer occlusion transcripts regulate the activity of human enhancer domains via transcriptional interference: a computational perspective." Nucleic Acids Research 48, no. 7 (March 5, 2020): 3435–54. http://dx.doi.org/10.1093/nar/gkaa026.

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Abstract Analysis of ENCODE long RNA-Seq and ChIP-seq (Chromatin Immunoprecipitation Sequencing) datasets for HepG2 and HeLa cell lines uncovered 1647 and 1958 transcripts that interfere with transcription factor binding to human enhancer domains. TFBSs (Transcription Factor Binding Sites) intersected by these ‘Enhancer Occlusion Transcripts’ (EOTrs) displayed significantly lower relative transcription factor (TF) binding affinities compared to TFBSs for the same TF devoid of EOTrs. Expression of most EOTrs was regulated in a cell line specific manner; analysis for the same TFBSs across cell lines, i.e. in the absence or presence of EOTrs, yielded consistently higher relative TF/DNA-binding affinities for TFBSs devoid of EOTrs. Lower activities of EOTr-associated enhancer domains coincided with reduced occupancy levels for histone tail modifications H3K27ac and H3K9ac. Similarly, the analysis of EOTrs with allele-specific expression identified lower activities for alleles associated with EOTrs. ChIA-PET (Chromatin Interaction Analysis by Paired-End Tag Sequencing) and 5C (Carbon Copy Chromosome Conformation Capture) uncovered that enhancer domains associated with EOTrs preferentially interacted with poised gene promoters. Analysis of EOTr regions with GRO-seq (Global run-on) data established the correlation of RNA polymerase pausing and occlusion of TF-binding. Our results implied that EOTr expression regulates human enhancer domains via transcriptional interference.
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Li, Peng, Suman Mitra, Rosanne Spolski, Jangsuk Oh, Wei Liao, Zhonghui Tang, Fei Mo, et al. "STAT5-mediated chromatin interactions in superenhancers activate IL-2 highly inducible genes: Functional dissection of the Il2ra gene locus." Proceedings of the National Academy of Sciences 114, no. 46 (October 24, 2017): 12111–19. http://dx.doi.org/10.1073/pnas.1714019114.

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Cytokines critically control immune responses, but how regulatory programs are altered to allow T cells to differentially respond to distinct cytokine stimuli remains poorly understood. Here, we have globally analyzed enhancer elements bound by IL-2–activated STAT5 and IL-21–activated STAT3 in T cells and identified Il2ra as the top-ranked gene regulated by an IL-2–activated STAT5-bound superenhancer and one of the top genes regulated by STAT3-bound superenhancers. Moreover, we found that STAT5 binding was rapidly superenriched at genes highly induced by IL-2 and that IL-2–activated STAT5 binding induces new and augmented chromatin interactions within superenhancer-containing genes. Based on chromatin interaction analysis by paired-end tag (ChIA-PET) sequencing data, we used CRISPR-Cas9 gene editing to target three of the STAT5 binding sites within the Il2ra superenhancer in mice. Each mutation decreased STAT5 binding and altered IL-2–induced Il2ra gene expression, revealing that individual elements within the superenhancer were not functionally redundant and that all were required for normal gene expression. Thus, we demonstrate cooperative utilization of superenhancer elements to optimize gene expression and show that STAT5 mediates IL-2–induced chromatin looping at superenhancers to preferentially regulate highly inducible genes, thereby providing new insights into the mechanisms underlying cytokine-dependent superenhancer function.
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Yu, Longtao, Hengxiang Shen, and Xiaowen Lyu. "Roles of Polycomb Complexes in the Reconstruction of 3D Genome Architecture during Preimplantation Embryonic Development." Genes 13, no. 12 (December 16, 2022): 2382. http://dx.doi.org/10.3390/genes13122382.

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The appropriate deployment of developmental programs depends on complex genetic information encoded by genomic DNA sequences and their positioning and contacts in the three-dimensional (3D) space within the nucleus. Current studies using novel techniques including, but not limited to, Hi-C, ChIA-PET, and Hi-ChIP reveal that regulatory elements (Res), such as enhancers and promoters, may participate in the precise regulation of expression of tissue-specific genes important for both embryogenesis and organogenesis by recruiting Polycomb Group (PcG) complexes. PcG complexes usually poise the transcription of developmental genes by forming Polycomb bodies to compact poised enhancers and promoters marked by H3K27me3 in the 3D space. Additionally, recent studies have also uncovered their roles in transcriptional activation. To better understand the full complexities in the mechanisms of how PcG complexes regulate transcription and long-range 3D contacts of enhancers and promoters during developmental programs, we outline novel insights regarding PcG-associated dramatic changes in the 3D chromatin conformation in developmental programs of early embryos and naïve-ground-state transitions of pluripotent embryonic stem cells (ESCs), and highlight the distinct roles of unique and common subunits of canonical and non-canonical PcG complexes in shaping genome architectures and transcriptional programs.
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Hovenga, Van, and Oluwatosin Oluwadare. "CBCR: A Curriculum Based Strategy For Chromosome Reconstruction." International Journal of Molecular Sciences 22, no. 8 (April 16, 2021): 4140. http://dx.doi.org/10.3390/ijms22084140.

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In this paper, we introduce a novel algorithm that aims to estimate chromosomes’ structure from their Hi-C contact data, called Curriculum Based Chromosome Reconstruction (CBCR). Specifically, our method performs this three dimensional reconstruction using cis-chromosomal interactions from Hi-C data. CBCR takes intra-chromosomal Hi-C interaction frequencies as an input and outputs a set of xyz coordinates that estimate the chromosome’s three dimensional structure in the form of a .pdb file. The algorithm relies on progressively training a distance-restraint-based algorithm with a strategy we refer to as curriculum learning. Curriculum learning divides the Hi-C data into classes based on contact frequency and progressively re-trains the distance-restraint algorithm based on the assumed importance of each curriculum in predicting the underlying chromosome structure. The distance-restraint algorithm relies on a modification of a Gaussian maximum likelihood function that scales probabilities based on the importance of features. We evaluate the performance of CBCR on both simulated and actual Hi-C data and perform validation on FISH, HiChIP, and ChIA-PET data as well. We also compare the performance of CBCR to several current methods. Our analysis shows that the use of curricula affects the rate of convergence of the optimization while decreasing the computational cost of our distance-restraint algorithm. Also, CBCR is more robust to increases in data resolution and therefore yields superior reconstruction accuracy of higher resolution data than all other methods in our comparison.
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Pyfrom, Sarah, Olivia Koues, Rodney Kowalewski, Eugene M. Oltz, and Jacqueline Payton. "Correlative Recurrent Expression of Predicted Elements (CREPE): A Novel Computational Approach to Predict LncRNA Function." Journal of Immunology 200, no. 1_Supplement (May 1, 2018): 167.10. http://dx.doi.org/10.4049/jimmunol.200.supp.167.10.

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Abstract Long non-coding RNAs (lncRNAs) act as transcriptional regulators, scaffolds, and signaling modulators in development, immune response, and oncogenesis. Our -omics study of >100 human Non-Hodgkin Lymphoma (NHL) and normal B cell samples revealed altered expression of lncRNAs in NHL. LncRNAs have not been characterized in NHL or B cells, and there are few guidelines for functional prediction. To address this gap, we developed a novel computational approach: Correlative Recurrent Expression of Predicted Elements (CREPE). This method calculates and tracks the following for each lncRNA: expression, cell-type specificity, subcellular localization, differential expression (eg, tumor/normal), correlation with neighboring gene transcripts (RNAseq); local enhancer and 3D genome interaction landscapes (ChIPseq, Hi-C, ChIA-PET). From this, CREPE calculates a rankable score to predict the likelihood of potential functions, including 1) transcriptional regulation via lncRNA transcript, 2) enhancer-associated regulation, and 3) scaffold/modulator cytoplasmic function. The likelihood score formula incorporates an if-then logic, correlative regression analysis, and statistical significance. Finally, CREPE enables heuristic ranking of lncRNAs by pathway analysis. CREPE identified lncRNAs that may regulate NHL oncogenes and modulate B-cell receptor signaling. CREPE analysis of data from The Cancer Genome Atlas and a CRISPRi lncRNA growth screen validate its ability to identify lncRNAs with oncogenic or growth-promoting potential in diverse studies. In summary, we developed a novel approach that fills an unmet need for predictive modeling of lncRNA function and identified lncRNAs likely to promote lymphomagenesis.
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Caudai, Claudia, Monica Zoppè, Anna Tonazzini, Ivan Merelli, and Emanuele Salerno. "Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct." Biology 10, no. 4 (April 16, 2021): 338. http://dx.doi.org/10.3390/biology10040338.

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The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct’s 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.
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Shih, Han-Yu, Chunhong Liu, ping wang, Sadie Signorella, William Montgomery, Dragana Jankovic, Hiroyuki Nagashima, et al. "A critical CTCF binding site of the Ifng-Il22 locus specifies cytokine expression and finetunes immune response." Journal of Immunology 206, no. 1_Supplement (May 1, 2021): 53.13. http://dx.doi.org/10.4049/jimmunol.206.supp.53.13.

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Abstract Precise control of cytokine milieu plays an essential role in homeostasis and diseases, and dysregulated cytokine production leads to undesired inflammation and autoimmunity. Interferon gamma (IFN-γ) and interleukin-22 (IL-22), two key cytokines for against intracellular and extracellular pathogens, respectively, evolutionarily resides in close genomic proximity. Notably, these genes are exclusively expressed in type I and type III lymphoid cells via complex epigenomic regulation that remains largely unknown. Our ATAC-seq, ChIP-seq and PollI ChIA-PET datasets revealed a CTCF binding site 70kb upstream of Ifng setting a boundary of the enhancer landscapes. Therefore, we proposed that this CTCF site segregates Ifng and Il22gene enhancers for lineage-specific gene regulation. To investigate whether this CTCF binding contributes to bifurcated cytokine gene regulation at the Ifng-Il22 locus, we deleted this 17bp Ifng (−70kb)CTCF site in mice using a CRISPR strategy. We found that Ifng (−70kb)CTCFdel CD4+ T cells revealed deficiency in initial Th1 cell differentiation. Using single cell RNA-seq and Toxoplasma gondii infection as a model for type I immune response in vivo, we identified aberrant Th17 differentiation and increased T cells prolifereation in Ifng (−70kb)CTCFdel mice. To further understand the molecular mechanism, we measured CTCF associated loop formation in pre-differentiated Naïve CD4+ T cells and interestingly, this Ifng (−70kb)CTCF site formed a loop with a CTCF-enriched region on the other side of Il22 gene. Our data provide a novel pre-determined mechanism that prevents cross activation for IFN-γ enhancers to target IL-22 prior to differentiation.
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Pagin, Miriam, Mattias Pernebrink, Simone Giubbolini, Cristiana Barone, Gaia Sambruni, Yanfen Zhu, Matteo Chiara, et al. "Sox2 Controls Neural Stem Cell Self-Renewal Through a Fos-Centered Gene Regulatory Network." Stem Cells 39, no. 8 (March 29, 2021): 1107–19. http://dx.doi.org/10.1002/stem.3373.

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Abstract The Sox2 transcription factor is necessary for the long-term self-renewal of neural stem cells (NSCs). Its mechanism of action is still poorly defined. To identify molecules regulated by Sox2, and acting in mouse NSC maintenance, we transduced, into Sox2-deleted NSC, genes whose expression is strongly downregulated following Sox2 loss (Fos, Jun, Egr2), individually or in combination. Fos alone rescued long-term proliferation, as shown by in vitro cell growth and clonal analysis. Furthermore, pharmacological inhibition by T-5224 of FOS/JUN AP1 complex binding to its targets decreased cell proliferation and expression of the putative target Suppressor of cytokine signaling 3 (Socs3). Additionally, Fos requirement for efficient long-term proliferation was demonstrated by the reduction of NSC clones capable of long-term expansion following CRISPR/Cas9-mediated Fos inactivation. Previous work showed that the Socs3 gene is strongly downregulated following Sox2 deletion, and its re-expression by lentiviral transduction rescues long-term NSC proliferation. Fos appears to be an upstream regulator of Socs3, possibly together with Jun and Egr2; indeed, Sox2 re-expression in Sox2-deleted NSC progressively activates both Fos and Socs3 expression; in turn, Fos transduction activates Socs3 expression. Based on available SOX2 ChIPseq and ChIA-PET data, we propose a model whereby Sox2 is a direct activator of both Socs3 and Fos, as well as possibly Jun and Egr2; furthermore, we provide direct evidence for FOS and JUN binding on Socs3 promoter, suggesting direct transcriptional regulation. These results provide the basis for developing a model of a network of interactions, regulating critical effectors of NSC proliferation and long-term maintenance.
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Mercurio, Sara, Giorgia Pozzolini, Roberta Baldi, Sara E. Barilà, Mattia Pitasi, Orazio Catona, Romina D’Aurizio, and Silvia K. Nicolis. "Hooked Up from a Distance: Charting Genome-Wide Long-Range Interaction Maps in Neural Cells Chromatin to Identify Novel Candidate Genes for Neurodevelopmental Disorders." International Journal of Molecular Sciences 24, no. 2 (January 6, 2023): 1164. http://dx.doi.org/10.3390/ijms24021164.

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DNA sequence variants (single nucleotide polymorphisms or variants, SNPs/SNVs; copy number variants, CNVs) associated to neurodevelopmental disorders (NDD) and traits often map on putative transcriptional regulatory elements, including, in particular, enhancers. However, the genes controlled by these enhancers remain poorly defined. Traditionally, the activity of a given enhancer, and the effect of its possible alteration associated to the sequence variants, has been thought to influence the nearest gene promoter. However, the obtainment of genome-wide long-range interaction maps in neural cells chromatin challenged this view, showing that a given enhancer is very frequently not connected to the nearest promoter, but to a more distant one, skipping genes in between. In this Perspective, we review some recent papers, who generated long-range interaction maps (by HiC, RNApolII ChIA-PET, Capture-HiC, or PLACseq), and overlapped the identified long-range interacting DNA segments with DNA sequence variants associated to NDD (such as schizophrenia, bipolar disorder and autism) and traits (intelligence). This strategy allowed to attribute the function of enhancers, hosting the NDD-related sequence variants, to a connected gene promoter lying far away on the linear chromosome map. Some of these enhancer-connected genes had indeed been already identified as contributive to the diseases, by the identification of mutations within the gene’s protein-coding regions (exons), validating the approach. Significantly, however, the connected genes also include many genes that were not previously found mutated in their exons, pointing to novel candidate contributors to NDD and traits. Thus, long-range interaction maps, in combination with DNA variants detected in association with NDD, can be used as “pointers” to identify novel candidate disease-relevant genes. Functional manipulation of the long-range interaction network involving enhancers and promoters by CRISPR-Cas9-based approaches is beginning to probe for the functional significance of the identified interactions, and the enhancers and the genes involved, improving our understanding of neural development and its pathology.
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D’Aurizio, Romina, Orazio Catona, Mattia Pitasi, Yang Eric Li, Bing Ren, and Silvia Kirsten Nicolis. "Bridging between Mouse and Human Enhancer-Promoter Long-Range Interactions in Neural Stem Cells, to Understand Enhancer Function in Neurodevelopmental Disease." International Journal of Molecular Sciences 23, no. 14 (July 19, 2022): 7964. http://dx.doi.org/10.3390/ijms23147964.

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Non-coding variation in complex human disease has been well established by genome-wide association studies, and is thought to involve regulatory elements, such as enhancers, whose variation affects the expression of the gene responsible for the disease. The regulatory elements often lie far from the gene they regulate, or within introns of genes differing from the regulated gene, making it difficult to identify the gene whose function is affected by a given enhancer variation. Enhancers are connected to their target gene promoters via long-range physical interactions (loops). In our study, we re-mapped, onto the human genome, more than 10,000 enhancers connected to promoters via long-range interactions, that we had previously identified in mouse brain-derived neural stem cells by RNApolII-ChIA-PET analysis, coupled to ChIP-seq mapping of DNA/chromatin regions carrying epigenetic enhancer marks. These interactions are thought to be functionally relevant. We discovered, in the human genome, thousands of DNA regions syntenic with the interacting mouse DNA regions (enhancers and connected promoters). We further annotated these human regions regarding their overlap with sequence variants (single nucleotide polymorphisms, SNPs; copy number variants, CNVs), that were previously associated with neurodevelopmental disease in humans. We document various cases in which the genetic variant, associated in humans to neurodevelopmental disease, affects an enhancer involved in long-range interactions: SNPs, previously identified by genome-wide association studies to be associated with schizophrenia, bipolar disorder, and intelligence, are located within our human syntenic enhancers, and alter transcription factor recognition sites. Similarly, CNVs associated to autism spectrum disease and other neurodevelopmental disorders overlap with our human syntenic enhancers. Some of these enhancers are connected (in mice) to homologs of genes already associated to the human disease, strengthening the hypothesis that the gene is indeed involved in the disease. Other enhancers are connected to genes not previously associated with the disease, pointing to their possible pathogenetic involvement. Our observations provide a resource for further exploration of neural disease, in parallel with the now widespread genome-wide identification of DNA variants in patients with neural disease.
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Ryan, Russell J. H., Jelena Petrovic, Dylan Rausch, Caleb Lareau, Winston Lee, Laura Donohue, Amanda L. Christie, et al. "Notch-Regulated Enhancers in B-Cell Lymphoma Activate MYC and Potentiate B-Cell Receptor Signaling." Blood 128, no. 22 (December 2, 2016): 457. http://dx.doi.org/10.1182/blood.v128.22.457.457.

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Abstract Gain-of-function mutations in Notch receptor genes occur in 10-15% of cases of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), and are associated with inferior clinical outcomes. Nearly all Notch mutations reported in B cell tumors lead to loss of the C-terminal negative regulatory PEST domain and result in stabilization of the activated form of Notch (intracellular Notch [ICN]), whereas mutations that lead to ligand-independent Notch activation (which are common in T cell acute lymphoblastic leukemia [T-ALL]) are rare. ICN can be detected in tumor cells within lymph nodes of >80% of patients with CLL, suggesting that Notch may have a broader oncogenic role than the incidence of Notch mutations would suggest. However, the downstream targets of Notch in B-cell tumors have not been identified. We used a gamma-secretase inhibitor (GSI) washout strategy to determine the immediate, direct effects of Notch activation in three MCL cell lines with Notch gain-of-function mutations, including two cell lines with unusual Notch gene rearrangements that lead to ligand-independent Notch activation, as well as a third line with a Notch PEST domain mutation in which signaling was activated with recombinant Notch ligand. Using these models, we identified likely direct target genes and their associated genomic Notch response elements using RNA-seq and ChIP-Seq in the Notch-on and Notch-off states. Most of these response elements corresponded to long-range enhancers that showed Notch-dependent changes in H3K27 acetylation, and were bound by components of the Notch transcription complex (NTC) in both cell lines. We confirmed these associations by performing ChIP-Seq on primary CLL and MCL biopsies, and by identifying specific looping interactions with Notch target gene promoters in public genome-wide proximity ligation datasets (RNA Pol2 ChIA-PET) from a lymphoblastoid cell line expressing the EBV-encoded Notch surrogate protein EBNA2. MYC was among the most strongly Notch-activated genes in Notch-dependent MCL cell lines and was associated with NTC binding at two B cell-specific 5' enhancers distinct from the Notch-dependent MYC enhancer previously identified in T-ALL. MCL cell line proliferation was blocked by Cas9 nuclease or epigenetic repressors targeting the 5' MYC enhancers, whereas cells were rescued from Notch inhibition by GSI via transduction with MYC. Gene set enrichment analysis of other direct Notch target genes identified in MCL models showed enrichment for regulators of B cell receptor (BCR) signaling, including the Src family kinase genes FYN, LYN, and BLK, and the signaling complex adaptor BLNK, as well as regulators of CD40 and cytokine signaling. RNA-seq analysis of primary CLL lymph node biopsies revealed significantly higher expression of many Notch target genes in biopsies with high levels of ICN. To functionally validate Notch target genes in primary tumors, we co-cultured CLL and MCL cells obtained from peripheral blood with Notch ligand-expressing stromal cells in the presence ("notch off") or absence ("notch on") of GSI, and demonstrated increased expression of Notch target genes, including MYC, in the "notch-on" cells. Furthermore, "notch-on" CLL cells showed increased phosphorylation of the BCR signaling intermediates SYK and PLCg2 upon BCR crosslinking compared to GSI-treated cells. Finally, we validated Notch-dependent regulation of target genes in vivo in a patient-derived xenograft model of NOTCH1-mutant MCL. Notch target gene expression was significantly higher in MCL cells within the spleen versus bone marrow or blood, but was markedly reduced in animals treated for five days with GSI. Additional xenograft studies are ongoing, and will be described at the meeting. Our data link active Notch signaling to two well-characterized oncogenic drivers in B cell lymphoma, MYC and BCR signaling, and may have important implications for the development of treatment strategies involving Notch antagonists and other targeted therapeutics, such as BCR targeting agents. Disclosures Weinstock: Novartis: Consultancy, Research Funding.
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46

Arega, Yibeltal, Hao Jiang, Shuangqi Wang, Jingwen Zhang, Xiaohui Niu, and Guoliang Li. "ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments." Frontiers in Genetics 11 (December 14, 2020). http://dx.doi.org/10.3389/fgene.2020.616160.

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Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.
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47

"ChIA-PET Elution Buffer." Cold Spring Harbor Protocols 2018, no. 8 (August 2018): pdb.rec104851. http://dx.doi.org/10.1101/pdb.rec104851.

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"ChIA-PET Wash Buffer." Cold Spring Harbor Protocols 2018, no. 8 (August 2018): pdb.rec104877. http://dx.doi.org/10.1101/pdb.rec104877.

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"TNE Buffer for ChIA-PET." Cold Spring Harbor Protocols 2018, no. 8 (August 2018): pdb.rec104901. http://dx.doi.org/10.1101/pdb.rec104901.

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"PCR Master Mix for ChIA-PET." Cold Spring Harbor Protocols 2018, no. 8 (August 2018): pdb.rec104935. http://dx.doi.org/10.1101/pdb.rec104935.

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