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Tang L, Liao J, Hill MC, Hu J, Zhao Y, Ellinor P, Li M. MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops. Nucleic Acids Res 2024; 52:e25. [PMID: 38281134 PMCID: PMC10954456 DOI: 10.1093/nar/gkae029] [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: 01/02/2023] [Revised: 12/03/2023] [Accepted: 01/12/2024] [Indexed: 01/30/2024] Open
Abstract
Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.
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Affiliation(s)
- Li Tang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jiaqi Liao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Matthew C Hill
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jiaxin Hu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping. Nat Commun 2023; 14:1208. [PMID: 36869052 PMCID: PMC9984425 DOI: 10.1038/s41467-023-36897-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/22/2023] [Indexed: 03/05/2023] Open
Abstract
Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity.
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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.
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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
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Closser M, Guo Y, Wang P, Patel T, Jang S, Hammelman J, De Nooij JC, Kopunova R, Mazzoni EO, Ruan Y, Gifford DK, Wichterle H. An expansion of the non-coding genome and its regulatory potential underlies vertebrate neuronal diversity. Neuron 2022; 110:70-85.e6. [PMID: 34727520 PMCID: PMC8738133 DOI: 10.1016/j.neuron.2021.10.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/25/2021] [Accepted: 10/06/2021] [Indexed: 01/07/2023]
Abstract
Proper assembly and function of the nervous system requires the generation of a uniquely diverse population of neurons expressing a cell-type-specific combination of effector genes that collectively define neuronal morphology, connectivity, and function. How countless partially overlapping but cell-type-specific patterns of gene expression are controlled at the genomic level remains poorly understood. Here we show that neuronal genes are associated with highly complex gene regulatory systems composed of independent cell-type- and cell-stage-specific regulatory elements that reside in expanded non-coding genomic domains. Mapping enhancer-promoter interactions revealed that motor neuron enhancers are broadly distributed across the large chromatin domains. This distributed regulatory architecture is not a unique property of motor neurons but is employed throughout the nervous system. The number of regulatory elements increased dramatically during the transition from invertebrates to vertebrates, suggesting that acquisition of new enhancers might be a fundamental process underlying the evolutionary increase in cellular complexity.
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Affiliation(s)
- Michael Closser
- Departments of Pathology and Cell Biology, Neuroscience, and Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Yuchun Guo
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Ping Wang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | - Tulsi Patel
- Departments of Pathology and Cell Biology, Neuroscience, and Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sumin Jang
- Departments of Pathology and Cell Biology, Neuroscience, and Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Jennifer Hammelman
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Joriene C De Nooij
- Department of Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Rachel Kopunova
- Departments of Pathology and Cell Biology, Neuroscience, and Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Yijun Ruan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | - David K Gifford
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
| | - Hynek Wichterle
- Departments of Pathology and Cell Biology, Neuroscience, and Neurology, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA.
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Krismer K, Guo Y, Gifford DK. IDR2D identifies reproducible genomic interactions. Nucleic Acids Res 2020; 48:e31. [PMID: 32009147 PMCID: PMC7102997 DOI: 10.1093/nar/gkaa030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/19/2019] [Accepted: 01/22/2020] [Indexed: 12/21/2022] Open
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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Affiliation(s)
- 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
| | - Yuchun Guo
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
| | - David K Gifford
- 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
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